Merge pull request #18551 from alalek:issue_17964

modules/dnn/src/dnn.cpp

@@ -2460,10 +2460,12 @@ struct Net::Impl : public detail::NetImplBase
                                     if( nextData )
                                         nextActivLayer = nextData->layerInstance.dynamicCast<ActivationLayer>();
 
+                                    Ptr<PowerLayer> activ_power;
                                     if( !nextActivLayer.empty() &&
                                             (!nextData->type.compare("ReLU") ||
                                              !nextData->type.compare("ChannelsPReLU") ||
-                                             !nextData->type.compare("Power")) &&
+                                             (!nextData->type.compare("Power") && (activ_power = nextActivLayer.dynamicCast<PowerLayer>()) && activ_power->scale == 1.0f)
+                                            ) &&
                                             currLayer->setActivation(nextActivLayer) )
                                     {
                                         CV_Assert_N(biasLayerData->outputBlobsWrappers.size() == 1, ld.inputBlobsWrappers.size() == 1);

modules/dnn/src/layers/convolution_layer.cpp

@@ -46,6 +46,8 @@
 #include "../op_inf_engine.hpp"
 #include "../ie_ngraph.hpp"
 
+#include <opencv2/core/utils/logger.hpp>
+
 #include "opencv2/core/hal/hal.hpp"
 #include "opencv2/core/hal/intrin.hpp"
 #include <iostream>
@@ -371,6 +373,14 @@ public:
             Ptr<PowerLayer> activ_power = activ.dynamicCast<PowerLayer>();
             if (!activ_power.empty())
             {
+                if (activ_power->scale != 1.0f)  // not supported well by implementation, #17964
+                {
+                    // FIXIT no way to check number of blobs (like, eltwise input)
+                    CV_LOG_INFO(NULL, "DNN/OpenCL: can't configure Power activation (scale != 1.0f)");
+                    activ.release();
+                    newActiv = false;
+                    return false;
+                }
                 if (activ_power->scale != 1.f || activ_power->shift != 0.f)
                 {
                     const int outCh = blobs[0].size[0];

modules/dnn/test/test_common.impl.hpp

@@ -63,10 +63,10 @@ void normAssert(
         double l1 /*= 0.00001*/, double lInf /*= 0.0001*/)
 {
     double normL1 = cvtest::norm(ref, test, cv::NORM_L1) / ref.getMat().total();
-    EXPECT_LE(normL1, l1) << comment;
+    EXPECT_LE(normL1, l1) << comment << "  |ref| = " << cvtest::norm(ref, cv::NORM_INF);
 
     double normInf = cvtest::norm(ref, test, cv::NORM_INF);
-    EXPECT_LE(normInf, lInf) << comment;
+    EXPECT_LE(normInf, lInf) << comment << "  |ref| = " << cvtest::norm(ref, cv::NORM_INF);
 }
 
 std::vector<cv::Rect2d> matToBoxes(const cv::Mat& m)

modules/dnn/test/test_layers.cpp

@@ -2219,10 +2219,6 @@ TEST_P(ConvolutionActivationFusion, Accuracy)
     Backend backendId = get<0>(get<2>(GetParam()));
     Target targetId = get<1>(get<2>(GetParam()));
 
-    // bug: https://github.com/opencv/opencv/issues/17964
-    if (actType == "Power" && backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
-
     Net net;
     int convId = net.addLayer(convParams.name, convParams.type, convParams);
     int activId = net.addLayerToPrev(activationParams.name, activationParams.type, activationParams);
@@ -2235,7 +2231,7 @@ TEST_P(ConvolutionActivationFusion, Accuracy)
             expectedFusedLayers.push_back(activId); // all activations are fused
         else if (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
         {
-            if (actType == "ReLU" || actType == "ChannelsPReLU" || actType == "ReLU6" || actType == "TanH" || actType == "Power")
+            if (actType == "ReLU" || actType == "ChannelsPReLU" || actType == "ReLU6" || actType == "TanH" /*|| actType == "Power"*/)
                 expectedFusedLayers.push_back(activId);
         }
     }
@@ -2349,10 +2345,6 @@ TEST_P(ConvolutionEltwiseActivationFusion, Accuracy)
     if ((eltwiseOp != "sum" || weightedEltwise) && backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
         applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
 
-    // bug: https://github.com/opencv/opencv/issues/17964
-    if (actType == "Power" && backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
-
     Net net;
     int convId = net.addLayer(convParams.name, convParams.type, convParams);
     int eltwiseId = net.addLayer(eltwiseParams.name, eltwiseParams.type, eltwiseParams);
@@ -2369,7 +2361,7 @@ TEST_P(ConvolutionEltwiseActivationFusion, Accuracy)
             expectedFusedLayers.push_back(activId); // activation is fused with eltwise layer
         else if (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
         {
-            if (actType == "ReLU" || actType == "ChannelsPReLU" || actType == "Power")
+            if (actType == "ReLU" || actType == "ChannelsPReLU" /*|| actType == "Power"*/)
             {
                 expectedFusedLayers.push_back(eltwiseId);
                 expectedFusedLayers.push_back(activId);
@@ -2431,10 +2423,6 @@ TEST_P(ConvolutionActivationEltwiseFusion, Accuracy)
     Backend backendId = get<0>(get<4>(GetParam()));
     Target targetId = get<1>(get<4>(GetParam()));
 
-    // bug: https://github.com/opencv/opencv/issues/17964
-    if (actType == "Power" && backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
-
     Net net;
     int convId = net.addLayer(convParams.name, convParams.type, convParams);
     int activId = net.addLayer(activationParams.name, activationParams.type, activationParams);
@@ -2451,7 +2439,7 @@ TEST_P(ConvolutionActivationEltwiseFusion, Accuracy)
             expectedFusedLayers.push_back(activId); // activation fused with convolution
         else if (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
         {
-            if (actType == "ReLU" || actType == "ChannelsPReLU" || actType == "ReLU6" || actType == "TanH" || actType == "Power")
+            if (actType == "ReLU" || actType == "ChannelsPReLU" || actType == "ReLU6" || actType == "TanH" /*|| actType == "Power"*/)
                 expectedFusedLayers.push_back(activId); // activation fused with convolution
         }
     }