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Merge pull request #17976 from YashasSamaga:dnn-fusion-tests-fix-ocl

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dnn: add exhaustive fusion tests, enable more eltwise fusions

* add eltwise fusion tests, enable more eltwise fusions

* merge weighted eltwise tests with eltwise tests
pull/18092/head
Yashas Samaga B L 4 years ago committed by GitHub
parent f3cebb3e1b
commit 2171cae8ff
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2 changed files with 433 additions and 1 deletions
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  1. 2
      modules/dnn/src/dnn.cpp
  2. 432
      modules/dnn/test/test_layers.cpp

2
modules/dnn/src/dnn.cpp
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@ -2458,7 +2458,7 @@ struct Net::Impl : public detail::NetImplBase
if( nextData )
nextActivLayer = nextData->layerInstance.dynamicCast<ActivationLayer>();
if( !nextActivLayer.empty() && pinsToKeep.count(lpNext) == 0 &&
if( !nextActivLayer.empty() &&
(!nextData->type.compare("ReLU") ||
!nextData->type.compare("ChannelsPReLU") ||
!nextData->type.compare("Power")) &&

432
modules/dnn/test/test_layers.cpp
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@ -2053,4 +2053,436 @@ TEST_P(Layer_Test_BatchNorm, fusion)
INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_BatchNorm, dnnBackendsAndTargets());
class TestLayerFusion : public DNNTestLayer {
public:
static void makeDefaultTestConvolutionLayer(LayerParams& convParams, int in_channels, int num_filters, bool bias_term)
{
const int kernel_h = 3, kernel_w = 3;
const int pad_h = kernel_h / 2, pad_w = kernel_w / 2;
convParams.set("kernel_h", kernel_h);
convParams.set("kernel_w", kernel_w);
convParams.set("pad_h", pad_h);
convParams.set("pad_w", pad_w);
convParams.set("num_output", num_filters);
convParams.set("bias_term", bias_term);
convParams.type = "Convolution";
convParams.name = "convolution";
float conv_init_magnitude = 1.0f / in_channels / kernel_h / kernel_w;
int weightsShape[] = {num_filters, in_channels, kernel_h, kernel_w};
Mat weights(4, &weightsShape[0], CV_32F);
randu(weights, -conv_init_magnitude, conv_init_magnitude);
convParams.blobs.push_back(weights);
if (bias_term)
{
Mat bias(1, num_filters, CV_32F);
randu(bias, -1.0f, 1.0f);
convParams.blobs.push_back(bias);
}
}
static void makeDefaultTestActivationLayer(LayerParams& activationParams, const std::string& type, int in_channels)
{
activationParams.type = type;
activationParams.name = "activation";
if (activationParams.type == "ReLU")
activationParams.set("negative_slope", 0.1f);
else if (activationParams.type == "Power")
{
activationParams.set("power", 2.0f);
activationParams.set("scale", 0.5f);
activationParams.set("shift", 0.3f);
}
else if (activationParams.type == "ReLU6")
{
activationParams.set("min_value", -1.0f);
activationParams.set("max_value", 1.0f);
}
else if (activationParams.type == "ChannelsPReLU")
{
Mat scales(1, in_channels, CV_32F);
randu(scales, -1.0f, 1.0f);
activationParams.blobs.push_back(scales);
}
}
static void makeDefaultTestEltwiseLayer(LayerParams& eltwiseParams, const std::string& op, bool withCoefficients)
{
eltwiseParams.type = "Eltwise";
eltwiseParams.name = "eltwise";
eltwiseParams.set("operation", op);
if (withCoefficients)
{
float coeff[] = {0.3f, 0.5f};
eltwiseParams.set("coeff", DictValue::arrayReal<float*>(coeff, 2));
}
}
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)
{
DNNTestLayer::checkBackend(backendId, targetId);
net.enableFusion(false);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(DNN_TARGET_CPU);
net.setInput(input);
Mat outputReference = net.forward().clone();
std::vector<double> refTimings;
net.getPerfProfile(refTimings);
for (int i = 0; i < refTimings.size(); i++)
{
CV_Assert(refTimings[i] != 0.0);
}
net.enableFusion(true);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
net.setInput(input);
Mat outputTest = net.forward().clone();
std::vector<double> testTimings;
net.getPerfProfile(testTimings);
for (int i = 0; i < testTimings.size(); i++)
{
if(std::find(expectedFusedLayers.begin(), expectedFusedLayers.end(), i + 1) != expectedFusedLayers.end())
{
EXPECT_EQ(testTimings[i], 0.0);
}
else
{
EXPECT_NE(testTimings[i], 0.0);
}
}
// double ref_max_value, ref_min_value;
// minMaxLoc(outputReference.reshape(1, 1), &ref_min_value, &ref_max_value);
// std::cout << "reference range: " << ref_min_value << ' ' << ref_max_value << std::endl;
double default_l1, default_lInf;
DNNTestLayer::getDefaultThresholds(backendId, targetId, &default_l1, &default_lInf);
if (l1 == 0.0)
l1 = default_l1;
if (lInf == 0.0)
lInf = default_lInf;
normAssert(outputReference, outputTest, "", l1, lInf);
}
static testing::internal::ParamGenerator<std::string> eltwiseOpList()
{
// TODO: automate list generation
return Values("sum", "max", "prod", "div");
}
static testing::internal::ParamGenerator<std::string> activationLayersList()
{
// TODO: automate list generation
return Values("ReLU", "ReLU6", "ChannelsPReLU", "TanH", "Swish", "Mish", "Sigmoid", "ELU", "AbsVal", "BNLL", "Power");
}
static testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargetsForFusionTests()
{
return dnnBackendsAndTargets(false, false, true, false); // OCV OpenCL + OCV CPU
}
};
typedef TestWithParam<tuple<bool, std::string, tuple<Backend, Target> > > ConvolutionActivationFusion;
TEST_P(ConvolutionActivationFusion, Accuracy)
{
// input
// |
// -----------------------
// | convolution |
// -----------------------
// |
// -----------------------
// | activation |
// -----------------------
// |
// output
const int batch_size = 2, in_channels = 16;
const int in_height = 16, in_width = 16;
int inputShape[] = {batch_size, in_channels, in_height, in_width};
Mat input(4, &inputShape[0], CV_32F);
randu(input, 1.0f, 2.0f);
bool bias_term = get<0>(GetParam());
LayerParams convParams;
TestLayerFusion::makeDefaultTestConvolutionLayer(convParams, in_channels, in_channels, bias_term);
std::string actType = get<1>(GetParam());
LayerParams activationParams;
TestLayerFusion::makeDefaultTestActivationLayer(activationParams, actType, in_channels);
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);
// bug: https://github.com/opencv/opencv/issues/17953
if (actType == "ChannelsPReLU" && bias_term == false &&
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);
net.connect(0, 0, convId, 0);
std::vector<int> expectedFusedLayers;
if (backendId == DNN_BACKEND_OPENCV)
{
if (targetId == DNN_TARGET_CPU)
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")
expectedFusedLayers.push_back(activId);
}
}
TestLayerFusion::test(input, net, backendId, targetId, expectedFusedLayers);
}
INSTANTIATE_TEST_CASE_P(TestLayerFusion, ConvolutionActivationFusion, Combine(
/* bias */ testing::Bool(),
/* activation */ TestLayerFusion::activationLayersList(),
TestLayerFusion::dnnBackendsAndTargetsForFusionTests()
));
typedef TestWithParam<tuple<bool, std::string, bool, tuple<Backend, Target> > > ConvolutionEltwiseFusion;
TEST_P(ConvolutionEltwiseFusion, Accuracy)
{
// input
// |
// -------------------------------
// | |
// | ---------------
// | | convolution |
// | ---------------
// | |
// | ---------------- |
// --------| eltwise op |-------
// ----------------
// |
// output
const int batch_size = 2, in_channels = 16;
const int in_height = 16, in_width = 16;
int inputShape[] = {batch_size, in_channels, in_height, in_width};
Mat input(4, &inputShape[0], CV_32F);
randu(input, 1.0f, 2.0f); // avoid small values to test eltwise div
bool bias_term = get<0>(GetParam());
LayerParams convParams;
TestLayerFusion::makeDefaultTestConvolutionLayer(convParams, in_channels, in_channels, bias_term);
std::string eltwiseOp = get<1>(GetParam());
bool weightedEltwise = get<2>(GetParam());
if (eltwiseOp != "sum" && weightedEltwise)
throw SkipTestException("weighted eltwise not supported");
LayerParams eltwiseParams;
TestLayerFusion::makeDefaultTestEltwiseLayer(eltwiseParams, eltwiseOp, weightedEltwise);
Net net;
int convId = net.addLayer(convParams.name, convParams.type, convParams);
int eltwiseId = net.addLayer(eltwiseParams.name, eltwiseParams.type, eltwiseParams);
net.connect(0, 0, convId, 0);
net.connect(convId, 0, eltwiseId, 0);
net.connect(0, 0, eltwiseId, 1);
Backend backendId = get<0>(get<3>(GetParam()));
Target targetId = get<1>(get<3>(GetParam()));
TestLayerFusion::test(input, net, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(TestLayerFusion, ConvolutionEltwiseFusion, Combine(
/* bias */ testing::Bool(),
/* eltwise op */ TestLayerFusion::eltwiseOpList(),
/* eltwise weighted */ testing::Bool(),
TestLayerFusion::dnnBackendsAndTargetsForFusionTests()
));
typedef TestWithParam<tuple<bool, std::string, bool, std::string, tuple<Backend, Target> > > ConvolutionEltwiseActivationFusion;
TEST_P(ConvolutionEltwiseActivationFusion, Accuracy)
{
// input
// |
// -------------------------------
// | |
// | ---------------
// | | convolution |
// | ---------------
// | |
// | ---------------- |
// --------| eltwise op |-------
// ----------------
// |
// ----------------
// | activation |
// ----------------
// |
// output
const int batch_size = 2, in_channels = 16;
const int in_height = 16, in_width = 16;
int inputShape[] = {batch_size, in_channels, in_height, in_width};
Mat input(4, &inputShape[0], CV_32F);
randu(input, 1.0f, 2.0f); // avoid small values to test eltwise div
bool bias_term = get<0>(GetParam());
LayerParams convParams;
TestLayerFusion::makeDefaultTestConvolutionLayer(convParams, in_channels, in_channels, bias_term);
std::string eltwiseOp = get<1>(GetParam());
bool weightedEltwise = get<2>(GetParam());
if (eltwiseOp != "sum" && weightedEltwise)
throw SkipTestException("weighted eltwise not supported");
LayerParams eltwiseParams;
TestLayerFusion::makeDefaultTestEltwiseLayer(eltwiseParams, eltwiseOp, false);
std::string actType = get<3>(GetParam());
LayerParams activationParams;
TestLayerFusion::makeDefaultTestActivationLayer(activationParams, actType, in_channels);
Backend backendId = get<0>(get<4>(GetParam()));
Target targetId = get<1>(get<4>(GetParam()));
// bug: https://github.com/opencv/opencv/issues/17945
if (eltwiseOp != "sum" && 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/17953
if (eltwiseOp == "sum" && actType == "ChannelsPReLU" && bias_term == false &&
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);
int activId = net.addLayer(activationParams.name, activationParams.type, activationParams);
net.connect(0, 0, convId, 0);
net.connect(convId, 0, eltwiseId, 0);
net.connect(0, 0, eltwiseId, 1);
net.connect(eltwiseId, 0, activId, 0);
std::vector<int> expectedFusedLayers;
if (backendId == DNN_BACKEND_OPENCV)
{
if (targetId == DNN_TARGET_CPU)
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")
{
expectedFusedLayers.push_back(eltwiseId);
expectedFusedLayers.push_back(activId);
}
}
}
TestLayerFusion::test(input, net, backendId, targetId, expectedFusedLayers);
}
INSTANTIATE_TEST_CASE_P(TestLayerFusion, ConvolutionEltwiseActivationFusion, Combine(
/* bias */ testing::Bool(),
/* eltwise op */ TestLayerFusion::eltwiseOpList(),
/* eltwise weighted */ testing::Bool(),
/* activation */ TestLayerFusion::activationLayersList(),
TestLayerFusion::dnnBackendsAndTargetsForFusionTests()
));
typedef TestWithParam<tuple<bool, std::string, std::string, bool, tuple<Backend, Target> > > ConvolutionActivationEltwiseFusion;
TEST_P(ConvolutionActivationEltwiseFusion, Accuracy)
{
// input
// |
// -------------------------------
// | |
// | ----------------
// | | convolution |
// | ----------------
// | |
// | ----------------
// | | activation |
// | ----------------
// | |
// | ---------------- |
// --------| eltwise sum |-------
// ----------------
// |
const int batch_size = 2, in_channels = 16;
const int in_height = 16, in_width = 16;
int inputShape[] = {batch_size, in_channels, in_height, in_width};
Mat input(4, &inputShape[0], CV_32F);
randu(input, 1.0f, 2.0f); // avoid small values to test eltwise div
bool bias_term = get<0>(GetParam());
LayerParams convParams;
TestLayerFusion::makeDefaultTestConvolutionLayer(convParams, in_channels, in_channels, bias_term);
std::string actType = get<1>(GetParam());
LayerParams activationParams;
TestLayerFusion::makeDefaultTestActivationLayer(activationParams, actType, in_channels);
std::string eltwiseOp = get<2>(GetParam());
bool weightedEltwise = get<3>(GetParam());
if (eltwiseOp != "sum" && weightedEltwise)
throw SkipTestException("weighted eltwise not supported");
LayerParams eltwiseParams;
TestLayerFusion::makeDefaultTestEltwiseLayer(eltwiseParams, eltwiseOp, false);
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);
// bug: https://github.com/opencv/opencv/issues/17953
if (actType == "ChannelsPReLU" && bias_term == false &&
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);
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

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