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dnn(opencl): bypass unsupported fusion cases

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This commit is contained in:
Alexander Alekhin
2020-10-09 11:57:49 +00:00
parent e87a0baa4b
commit 718dd9f170
4 changed files with 18 additions and 18 deletions
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modules/dnn/test/test_layers.cpp
+3 -15
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@@ -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
}
}