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Merge pull request #17976 from YashasSamaga:dnn-fusion-tests-fix-ocl
dnn: add exhaustive fusion tests, enable more eltwise fusions * add eltwise fusion tests, enable more eltwise fusions * merge weighted eltwise tests with eltwise tests
This commit is contained in:
Yashas Samaga B L
GitHub
parent
f3cebb3e1b
commit
2171cae8ff
@ -2458,7 +2458,7 @@ struct Net::Impl : public detail::NetImplBase
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if( nextData )
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nextActivLayer = nextData->layerInstance.dynamicCast<ActivationLayer>();
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if( !nextActivLayer.empty() && pinsToKeep.count(lpNext) == 0 &&
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if( !nextActivLayer.empty() &&
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(!nextData->type.compare("ReLU") ||
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!nextData->type.compare("ChannelsPReLU") ||
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!nextData->type.compare("Power")) &&
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@ -2053,4 +2053,436 @@ TEST_P(Layer_Test_BatchNorm, fusion)
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INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_BatchNorm, dnnBackendsAndTargets());
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class TestLayerFusion : public DNNTestLayer {
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public:
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static void makeDefaultTestConvolutionLayer(LayerParams& convParams, int in_channels, int num_filters, bool bias_term)
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{
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const int kernel_h = 3, kernel_w = 3;
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const int pad_h = kernel_h / 2, pad_w = kernel_w / 2;
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convParams.set("kernel_h", kernel_h);
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convParams.set("kernel_w", kernel_w);
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convParams.set("pad_h", pad_h);
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convParams.set("pad_w", pad_w);
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convParams.set("num_output", num_filters);
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convParams.set("bias_term", bias_term);
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convParams.type = "Convolution";
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convParams.name = "convolution";
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float conv_init_magnitude = 1.0f / in_channels / kernel_h / kernel_w;
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int weightsShape[] = {num_filters, in_channels, kernel_h, kernel_w};
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Mat weights(4, &weightsShape[0], CV_32F);
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randu(weights, -conv_init_magnitude, conv_init_magnitude);
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convParams.blobs.push_back(weights);
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if (bias_term)
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{
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Mat bias(1, num_filters, CV_32F);
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randu(bias, -1.0f, 1.0f);
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convParams.blobs.push_back(bias);
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}
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}
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static void makeDefaultTestActivationLayer(LayerParams& activationParams, const std::string& type, int in_channels)
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{
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activationParams.type = type;
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activationParams.name = "activation";
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if (activationParams.type == "ReLU")
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activationParams.set("negative_slope", 0.1f);
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else if (activationParams.type == "Power")
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{
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activationParams.set("power", 2.0f);
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activationParams.set("scale", 0.5f);
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activationParams.set("shift", 0.3f);
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}
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else if (activationParams.type == "ReLU6")
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{
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activationParams.set("min_value", -1.0f);
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activationParams.set("max_value", 1.0f);
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}
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else if (activationParams.type == "ChannelsPReLU")
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{
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Mat scales(1, in_channels, CV_32F);
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randu(scales, -1.0f, 1.0f);
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activationParams.blobs.push_back(scales);
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}
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}
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static void makeDefaultTestEltwiseLayer(LayerParams& eltwiseParams, const std::string& op, bool withCoefficients)
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{
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eltwiseParams.type = "Eltwise";
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eltwiseParams.name = "eltwise";
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eltwiseParams.set("operation", op);
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if (withCoefficients)
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{
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float coeff[] = {0.3f, 0.5f};
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eltwiseParams.set("coeff", DictValue::arrayReal<float*>(coeff, 2));
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}
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}
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static void test(Mat& input, Net& net, Backend backendId, Target targetId, std::vector<int> expectedFusedLayers = std::vector<int>(), double l1 = 0.0, double lInf = 0.0)
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{
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DNNTestLayer::checkBackend(backendId, targetId);
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net.enableFusion(false);
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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net.setPreferableTarget(DNN_TARGET_CPU);
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net.setInput(input);
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Mat outputReference = net.forward().clone();
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std::vector<double> refTimings;
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net.getPerfProfile(refTimings);
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for (int i = 0; i < refTimings.size(); i++)
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{
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CV_Assert(refTimings[i] != 0.0);
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}
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net.enableFusion(true);
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net.setPreferableBackend(backendId);
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net.setPreferableTarget(targetId);
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net.setInput(input);
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Mat outputTest = net.forward().clone();
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std::vector<double> testTimings;
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net.getPerfProfile(testTimings);
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for (int i = 0; i < testTimings.size(); i++)
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{
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if(std::find(expectedFusedLayers.begin(), expectedFusedLayers.end(), i + 1) != expectedFusedLayers.end())
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{
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EXPECT_EQ(testTimings[i], 0.0);
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}
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else
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{
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EXPECT_NE(testTimings[i], 0.0);
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}
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}
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// double ref_max_value, ref_min_value;
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// minMaxLoc(outputReference.reshape(1, 1), &ref_min_value, &ref_max_value);
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// std::cout << "reference range: " << ref_min_value << ' ' << ref_max_value << std::endl;
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double default_l1, default_lInf;
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DNNTestLayer::getDefaultThresholds(backendId, targetId, &default_l1, &default_lInf);
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if (l1 == 0.0)
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l1 = default_l1;
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if (lInf == 0.0)
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lInf = default_lInf;
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normAssert(outputReference, outputTest, "", l1, lInf);
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}
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static testing::internal::ParamGenerator<std::string> eltwiseOpList()
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{
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// TODO: automate list generation
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return Values("sum", "max", "prod", "div");
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}
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static testing::internal::ParamGenerator<std::string> activationLayersList()
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{
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// TODO: automate list generation
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return Values("ReLU", "ReLU6", "ChannelsPReLU", "TanH", "Swish", "Mish", "Sigmoid", "ELU", "AbsVal", "BNLL", "Power");
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}
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static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargetsForFusionTests()
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{
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return dnnBackendsAndTargets(false, false, true, false); // OCV OpenCL + OCV CPU
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}
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};
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typedef TestWithParam<tuple<bool, std::string, tuple<Backend, Target> > > ConvolutionActivationFusion;
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TEST_P(ConvolutionActivationFusion, Accuracy)
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{
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// input
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// |
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// -----------------------
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// | convolution |
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// -----------------------
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// |
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// -----------------------
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// | activation |
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// -----------------------
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// |
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// output
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const int batch_size = 2, in_channels = 16;
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const int in_height = 16, in_width = 16;
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int inputShape[] = {batch_size, in_channels, in_height, in_width};
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Mat input(4, &inputShape[0], CV_32F);
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randu(input, 1.0f, 2.0f);
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bool bias_term = get<0>(GetParam());
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LayerParams convParams;
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TestLayerFusion::makeDefaultTestConvolutionLayer(convParams, in_channels, in_channels, bias_term);
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std::string actType = get<1>(GetParam());
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LayerParams activationParams;
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TestLayerFusion::makeDefaultTestActivationLayer(activationParams, actType, in_channels);
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Backend backendId = get<0>(get<2>(GetParam()));
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Target targetId = get<1>(get<2>(GetParam()));
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// bug: https://github.com/opencv/opencv/issues/17964
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if (actType == "Power" && backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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// bug: https://github.com/opencv/opencv/issues/17953
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if (actType == "ChannelsPReLU" && bias_term == false &&
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backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
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{
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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}
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Net net;
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int convId = net.addLayer(convParams.name, convParams.type, convParams);
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int activId = net.addLayerToPrev(activationParams.name, activationParams.type, activationParams);
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net.connect(0, 0, convId, 0);
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std::vector<int> expectedFusedLayers;
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if (backendId == DNN_BACKEND_OPENCV)
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{
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if (targetId == DNN_TARGET_CPU)
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expectedFusedLayers.push_back(activId); // all activations are fused
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else if (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
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{
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if (actType == "ReLU" || actType == "ChannelsPReLU" || actType == "ReLU6" || actType == "TanH" || actType == "Power")
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expectedFusedLayers.push_back(activId);
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}
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}
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TestLayerFusion::test(input, net, backendId, targetId, expectedFusedLayers);
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}
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INSTANTIATE_TEST_CASE_P(TestLayerFusion, ConvolutionActivationFusion, Combine(
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/* bias */ testing::Bool(),
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/* activation */ TestLayerFusion::activationLayersList(),
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TestLayerFusion::dnnBackendsAndTargetsForFusionTests()
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));
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typedef TestWithParam<tuple<bool, std::string, bool, tuple<Backend, Target> > > ConvolutionEltwiseFusion;
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TEST_P(ConvolutionEltwiseFusion, Accuracy)
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{
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// input
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// |
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// -------------------------------
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// | |
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// | ---------------
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// | | convolution |
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// | ---------------
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// | |
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// | ---------------- |
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// --------| eltwise op |-------
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// ----------------
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// |
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// output
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const int batch_size = 2, in_channels = 16;
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const int in_height = 16, in_width = 16;
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int inputShape[] = {batch_size, in_channels, in_height, in_width};
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Mat input(4, &inputShape[0], CV_32F);
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randu(input, 1.0f, 2.0f); // avoid small values to test eltwise div
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bool bias_term = get<0>(GetParam());
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LayerParams convParams;
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TestLayerFusion::makeDefaultTestConvolutionLayer(convParams, in_channels, in_channels, bias_term);
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std::string eltwiseOp = get<1>(GetParam());
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bool weightedEltwise = get<2>(GetParam());
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if (eltwiseOp != "sum" && weightedEltwise)
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throw SkipTestException("weighted eltwise not supported");
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LayerParams eltwiseParams;
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TestLayerFusion::makeDefaultTestEltwiseLayer(eltwiseParams, eltwiseOp, weightedEltwise);
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Net net;
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int convId = net.addLayer(convParams.name, convParams.type, convParams);
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int eltwiseId = net.addLayer(eltwiseParams.name, eltwiseParams.type, eltwiseParams);
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net.connect(0, 0, convId, 0);
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net.connect(convId, 0, eltwiseId, 0);
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net.connect(0, 0, eltwiseId, 1);
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Backend backendId = get<0>(get<3>(GetParam()));
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Target targetId = get<1>(get<3>(GetParam()));
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TestLayerFusion::test(input, net, backendId, targetId);
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}
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INSTANTIATE_TEST_CASE_P(TestLayerFusion, ConvolutionEltwiseFusion, Combine(
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/* bias */ testing::Bool(),
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/* eltwise op */ TestLayerFusion::eltwiseOpList(),
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/* eltwise weighted */ testing::Bool(),
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TestLayerFusion::dnnBackendsAndTargetsForFusionTests()
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));
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typedef TestWithParam<tuple<bool, std::string, bool, std::string, tuple<Backend, Target> > > ConvolutionEltwiseActivationFusion;
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TEST_P(ConvolutionEltwiseActivationFusion, Accuracy)
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{
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// input
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// |
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// -------------------------------
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// | |
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// | ---------------
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// | | convolution |
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// | ---------------
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// | |
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// | ---------------- |
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// --------| eltwise op |-------
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// ----------------
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// |
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// ----------------
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// | activation |
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// ----------------
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// |
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// output
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const int batch_size = 2, in_channels = 16;
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const int in_height = 16, in_width = 16;
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int inputShape[] = {batch_size, in_channels, in_height, in_width};
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Mat input(4, &inputShape[0], CV_32F);
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randu(input, 1.0f, 2.0f); // avoid small values to test eltwise div
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bool bias_term = get<0>(GetParam());
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LayerParams convParams;
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TestLayerFusion::makeDefaultTestConvolutionLayer(convParams, in_channels, in_channels, bias_term);
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std::string eltwiseOp = get<1>(GetParam());
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bool weightedEltwise = get<2>(GetParam());
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if (eltwiseOp != "sum" && weightedEltwise)
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throw SkipTestException("weighted eltwise not supported");
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LayerParams eltwiseParams;
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TestLayerFusion::makeDefaultTestEltwiseLayer(eltwiseParams, eltwiseOp, false);
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std::string actType = get<3>(GetParam());
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LayerParams activationParams;
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TestLayerFusion::makeDefaultTestActivationLayer(activationParams, actType, in_channels);
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Backend backendId = get<0>(get<4>(GetParam()));
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Target targetId = get<1>(get<4>(GetParam()));
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// bug: https://github.com/opencv/opencv/issues/17945
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if (eltwiseOp != "sum" && backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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// bug: https://github.com/opencv/opencv/issues/17953
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if (eltwiseOp == "sum" && actType == "ChannelsPReLU" && bias_term == false &&
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backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
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{
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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}
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// bug: https://github.com/opencv/opencv/issues/17964
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if (actType == "Power" && backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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Net net;
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int convId = net.addLayer(convParams.name, convParams.type, convParams);
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int eltwiseId = net.addLayer(eltwiseParams.name, eltwiseParams.type, eltwiseParams);
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int activId = net.addLayer(activationParams.name, activationParams.type, activationParams);
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net.connect(0, 0, convId, 0);
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net.connect(convId, 0, eltwiseId, 0);
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net.connect(0, 0, eltwiseId, 1);
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net.connect(eltwiseId, 0, activId, 0);
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std::vector<int> expectedFusedLayers;
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if (backendId == DNN_BACKEND_OPENCV)
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{
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if (targetId == DNN_TARGET_CPU)
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expectedFusedLayers.push_back(activId); // activation is fused with eltwise layer
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else if (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
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{
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if (actType == "ReLU" || actType == "ChannelsPReLU" || actType == "Power")
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{
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expectedFusedLayers.push_back(eltwiseId);
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expectedFusedLayers.push_back(activId);
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}
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}
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}
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TestLayerFusion::test(input, net, backendId, targetId, expectedFusedLayers);
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}
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INSTANTIATE_TEST_CASE_P(TestLayerFusion, ConvolutionEltwiseActivationFusion, Combine(
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/* bias */ testing::Bool(),
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/* eltwise op */ TestLayerFusion::eltwiseOpList(),
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/* eltwise weighted */ testing::Bool(),
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/* activation */ TestLayerFusion::activationLayersList(),
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TestLayerFusion::dnnBackendsAndTargetsForFusionTests()
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));
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typedef TestWithParam<tuple<bool, std::string, std::string, bool, tuple<Backend, Target> > > ConvolutionActivationEltwiseFusion;
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TEST_P(ConvolutionActivationEltwiseFusion, Accuracy)
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{
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// input
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// |
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// -------------------------------
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// | |
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// | ----------------
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// | | convolution |
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// | ----------------
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// | |
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// | ----------------
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// | | activation |
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// | ----------------
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// | |
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// | ---------------- |
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// --------| eltwise sum |-------
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// ----------------
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// |
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const int batch_size = 2, in_channels = 16;
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const int in_height = 16, in_width = 16;
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int inputShape[] = {batch_size, in_channels, in_height, in_width};
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Mat input(4, &inputShape[0], CV_32F);
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randu(input, 1.0f, 2.0f); // avoid small values to test eltwise div
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bool bias_term = get<0>(GetParam());
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LayerParams convParams;
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TestLayerFusion::makeDefaultTestConvolutionLayer(convParams, in_channels, in_channels, bias_term);
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std::string actType = get<1>(GetParam());
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LayerParams activationParams;
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TestLayerFusion::makeDefaultTestActivationLayer(activationParams, actType, in_channels);
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std::string eltwiseOp = get<2>(GetParam());
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bool weightedEltwise = get<3>(GetParam());
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if (eltwiseOp != "sum" && weightedEltwise)
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throw SkipTestException("weighted eltwise not supported");
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LayerParams eltwiseParams;
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TestLayerFusion::makeDefaultTestEltwiseLayer(eltwiseParams, eltwiseOp, false);
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Backend backendId = get<0>(get<4>(GetParam()));
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Target targetId = get<1>(get<4>(GetParam()));
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// bug: https://github.com/opencv/opencv/issues/17964
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if (actType == "Power" && backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
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// bug: https://github.com/opencv/opencv/issues/17953
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if (actType == "ChannelsPReLU" && bias_term == false &&
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backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
|
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{
|
||||
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);
|
||||
int eltwiseId = net.addLayer(eltwiseParams.name, eltwiseParams.type, eltwiseParams);
|
||||
net.connect(0, 0, convId, 0);
|
||||
net.connect(convId, 0, activId, 0);
|
||||
net.connect(activId, 0, eltwiseId, 0);
|
||||
net.connect(0, 0, eltwiseId, 1);
|
||||
|
||||
std::vector<int> expectedFusedLayers;
|
||||
if (backendId == DNN_BACKEND_OPENCV)
|
||||
{
|
||||
if (targetId == DNN_TARGET_CPU)
|
||||
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")
|
||||
expectedFusedLayers.push_back(activId); // activation fused with convolution
|
||||
}
|
||||
}
|
||||
|
||||
TestLayerFusion::test(input, net, backendId, targetId, expectedFusedLayers);
|
||||
}
|
||||
INSTANTIATE_TEST_CASE_P(TestLayerFusion, ConvolutionActivationEltwiseFusion, Combine(
|
||||
/* bias */ testing::Bool(),
|
||||
/* activation */ TestLayerFusion::activationLayersList(),
|
||||
/* eltwise op */ TestLayerFusion::eltwiseOpList(),
|
||||
/* eltwise weighted */ testing::Bool(),
|
||||
TestLayerFusion::dnnBackendsAndTargetsForFusionTests()
|
||||
));
|
||||
|
||||
}} // namespace
|
||||
|
||||
Loading…
Reference in New Issue
Block a user