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opencv/modules/dnn/test/test_halide_layers.cpp
Dmitry Kurtaev cad7c4d51d MobileNet-SSD and VGG-SSD topologies in Halide
2017-09-08 09:55:53 +03:00

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2017, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
// This tests doesn't require any external data. They just compare outputs of
// layers using different computation backends. Input and parameters are random.
#include "test_precomp.hpp"
namespace cvtest
{
#ifdef HAVE_HALIDE
using namespace cv;
using namespace cv::dnn;
using namespace testing;
static void test(LayerParams& params, Mat& input)
{
randu(input, -1.0f, 1.0f);
Net net;
int lid = net.addLayer(params.name, params.type, params);
net.connect(0, 0, lid, 0);
net.setInput(input);
Mat outputDefault = net.forward(params.name).clone();
net.setPreferableBackend(DNN_BACKEND_HALIDE);
Mat outputHalide = net.forward(params.name).clone();
normAssert(outputDefault, outputHalide);
}
////////////////////////////////////////////////////////////////////////////////
// Convolution
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Size, bool> > Convolution;
TEST_P(Convolution, Accuracy)
{
int inChannels = get<0>(GetParam())[0];
int outChannels = get<0>(GetParam())[1];
int group = get<0>(GetParam())[2];
Size inSize = get<1>(GetParam());
Size kernel = get<2>(GetParam());
Size stride = get<3>(GetParam());
Size pad = get<4>(GetParam());
Size dilation = get<5>(GetParam());
bool hasBias = get<6>(GetParam());
Mat weights({outChannels, inChannels / group, kernel.height, kernel.width}, CV_32F);
randu(weights, -1.0f, 1.0f);
LayerParams lp;
lp.set("kernel_w", kernel.width);
lp.set("kernel_h", kernel.height);
lp.set("pad_w", pad.width);
lp.set("pad_h", pad.height);
lp.set("stride_w", stride.width);
lp.set("stride_h", stride.height);
lp.set("dilation_w", dilation.width);
lp.set("dilation_h", dilation.height);
lp.set("num_output", outChannels);
lp.set("group", group);
lp.set("bias_term", hasBias);
lp.type = "Convolution";
lp.name = "testLayer";
lp.blobs.push_back(weights);
if (hasBias)
{
Mat bias({outChannels}, CV_32F);
randu(bias, -1.0f, 1.0f);
lp.blobs.push_back(bias);
}
Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F);
test(lp, input);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Convolution, Combine(
/*in channels, out channels, group*/
Values(Vec3i(6, 4, 1), Vec3i(6, 9, 1),
Vec3i(6, 4, 2), Vec3i(6, 9, 3)),
/*in size*/ Values(Size(5, 6)),
/*kernel*/ Values(Size(3, 1), Size(1, 3)),
/*stride*/ Values(Size(1, 1), Size(2, 2)),
/*pad*/ Values(Size(1, 0), Size(0, 1)),
/*dilation*/ Values(Size(1, 1), Size(2, 2)),
/*has bias*/ Bool()
));
////////////////////////////////////////////////////////////////////////////////
// Deconvolution
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Vec4i, bool> > Deconvolution;
TEST_P(Deconvolution, Accuracy)
{
int inChannels = get<0>(GetParam())[0];
int outChannels = get<0>(GetParam())[1];
int group = get<0>(GetParam())[2];
Size inSize = get<1>(GetParam());
Size kernel = get<2>(GetParam());
Size pad = get<3>(GetParam());
Size dilation = get<4>(GetParam());
Size stride = Size(get<5>(GetParam())[0], get<5>(GetParam())[1]);
Size adjPad = Size(get<5>(GetParam())[2], get<5>(GetParam())[3]);
bool hasBias = get<6>(GetParam());
Mat weights({outChannels, inChannels / group, kernel.height, kernel.width}, CV_32F);
randu(weights, -1.0f, 1.0f);
LayerParams lp;
lp.set("kernel_w", kernel.width);
lp.set("kernel_h", kernel.height);
lp.set("pad_w", pad.width);
lp.set("pad_h", pad.height);
lp.set("stride_w", stride.width);
lp.set("stride_h", stride.height);
lp.set("dilation_w", dilation.width);
lp.set("dilation_h", dilation.height);
lp.set("adj_w", adjPad.width);
lp.set("adj_h", adjPad.height);
lp.set("num_output", outChannels);
lp.set("group", group);
lp.set("bias_term", hasBias);
lp.type = "Deconvolution";
lp.name = "testLayer";
lp.blobs.push_back(weights);
if (hasBias)
{
Mat bias({outChannels}, CV_32F);
randu(bias, -1.0f, 1.0f);
lp.blobs.push_back(bias);
}
Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F);
test(lp, input);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Deconvolution, Combine(
/*in channels, out channels, group*/
Values(Vec3i(6, 4, 1), Vec3i(6, 9, 1)),
/*in size*/ Values(Size(5, 6)),
/*kernel*/ Values(Size(3, 1), Size(1, 3)),
/*pad*/ Values(Size(1, 0), Size(0, 1)),
/*dilation*/ Values(Size(1, 1), Size(2, 2)),
/*stride, adj. pad*/ Values(Vec4i(1,1, 0,0), Vec4i(2,2, 1,0), Vec4i(1,2, 0,1)),
/*has bias*/ Bool()
));
////////////////////////////////////////////////////////////////////////////////
// LRN
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<Vec3i, int, Vec3f, bool, std::string> > LRN;
TEST_P(LRN, Accuracy)
{
int inChannels = get<0>(GetParam())[0];
Size inSize = Size(get<0>(GetParam())[1], get<0>(GetParam())[2]);
int localSize = get<1>(GetParam());
float alpha = get<2>(GetParam())[0];
float beta = get<2>(GetParam())[1];
float bias = get<2>(GetParam())[2];
bool normBySize = get<3>(GetParam());
std::string nrmType = get<4>(GetParam());
LayerParams lp;
lp.set("norm_region", nrmType);
lp.set("local_size", localSize);
lp.set("alpha", alpha);
lp.set("beta", beta);
lp.set("bias", bias);
lp.set("norm_by_size", normBySize);
lp.type = "LRN";
lp.name = "testLayer";
Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F);
test(lp, input);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, LRN, Combine(
/*input ch,w,h*/ Values(Vec3i(6, 5, 8), Vec3i(7, 11, 6)),
/*local size*/ Values(3, 5),
Values(Vec3f(0.9f, 1.0f, 1.1f), Vec3f(0.9f, 1.1f, 1.0f),
/*alpha, beta,*/ Vec3f(1.0f, 0.9f, 1.1f), Vec3f(1.0f, 1.1f, 0.9f),
/*bias */ Vec3f(1.1f, 0.9f, 1.0f), Vec3f(1.1f, 1.0f, 0.9f)),
/*norm_by_size*/ Bool(),
/*norm_type*/ Values("ACROSS_CHANNELS", "WITHIN_CHANNEL")
));
////////////////////////////////////////////////////////////////////////////////
// Average pooling
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int, Size, Size, Size> > AvePooling;
TEST_P(AvePooling, Accuracy)
{
int inChannels = get<0>(GetParam());
Size outSize = get<1>(GetParam());; // Input size will be computed from parameters.
Size kernel = get<2>(GetParam());
Size stride = get<3>(GetParam());
const int inWidth = (outSize.width - 1) * stride.width + kernel.width;
const int inHeight = (outSize.height - 1) * stride.height + kernel.height;
LayerParams lp;
lp.set("pool", "ave");
lp.set("kernel_w", kernel.width);
lp.set("kernel_h", kernel.height);
lp.set("stride_w", stride.width);
lp.set("stride_h", stride.height);
lp.type = "Pooling";
lp.name = "testLayer";
Mat input({1, inChannels, inHeight, inWidth}, CV_32F);
test(lp, input);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, AvePooling, Combine(
/*in channels*/ Values(3, 4),
/*out size*/ Values(Size(1, 1), Size(2, 2), Size(3, 2), Size(4, 7)),
/*kernel*/ Values(Size(1, 1), Size(2, 2), Size(3, 3), Size(3, 2)),
/*stride*/ Values(Size(1, 1), Size(2, 2), Size(3, 2))
));
////////////////////////////////////////////////////////////////////////////////
// Maximum pooling
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int, Size, Size, Size, Size> > MaxPooling;
TEST_P(MaxPooling, Accuracy)
{
int inChannels = get<0>(GetParam());
Size inSize = get<1>(GetParam());
Size kernel = get<2>(GetParam());
Size stride = get<3>(GetParam());
Size pad = get<4>(GetParam());
LayerParams lp;
lp.set("pool", "max");
lp.set("kernel_w", kernel.width);
lp.set("kernel_h", kernel.height);
lp.set("stride_w", stride.width);
lp.set("stride_h", stride.height);
lp.set("pad_w", pad.width);
lp.set("pad_h", pad.height);
lp.type = "Pooling";
lp.name = "testLayer";
Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F);
test(lp, input);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, MaxPooling, Combine(
/*in channels*/ Values(3, 4),
/*in size*/ Values(Size(5, 5), Size(7, 6)),
/*kernel*/ Values(Size(2, 2), Size(3, 3), Size(3, 2)),
/*stride*/ Values(Size(1, 1), Size(2, 2), Size(3, 2)),
/*pad*/ Values(Size(0, 0), Size(1, 1), Size(0, 1))
));
////////////////////////////////////////////////////////////////////////////////
// Fully-connected
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int, Size, int, bool> > FullyConnected;
TEST_P(FullyConnected, Accuracy)
{
int inChannels = get<0>(GetParam());
Size inSize = get<1>(GetParam());
int outChannels = get<2>(GetParam());
bool hasBias = get<3>(GetParam());
Mat weights(outChannels, inChannels * inSize.height * inSize.width, CV_32F);
randu(weights, -1.0f, 1.0f);
Mat bias(1, outChannels, CV_32F);
randu(bias, -1.0f, 1.0f);
LayerParams lp;
lp.set("num_output", outChannels);
lp.set("bias_term", hasBias);
lp.blobs.push_back(weights);
lp.blobs.push_back(bias);
lp.type = "InnerProduct";
lp.name = "testLayer";
Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F);
test(lp, input);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, FullyConnected, Combine(
/*in channels*/ Values(3, 4),
/*in size*/ Values(Size(5, 4), Size(4, 5), Size(1, 1)),
/*out channels*/ Values(3, 4),
/*has bias*/ Bool()
));
////////////////////////////////////////////////////////////////////////////////
// SoftMax
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int> > SoftMax;
TEST_P(SoftMax, Accuracy)
{
int inChannels = get<0>(GetParam());
LayerParams lp;
lp.type = "SoftMax";
lp.name = "testLayer";
Mat input({1, inChannels, 1, 1}, CV_32F);
test(lp, input);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, SoftMax, Values(3, 4, 5, 1024));
//////////////////////////////////////////////////////////////////////////////
// Max pooling - unpooling
//////////////////////////////////////////////////////////////////////////////
TEST(MaxPoolUnpool_Halide, Accuracy)
{
LayerParams pool;
pool.set("pool", "max");
pool.set("kernel_w", 2);
pool.set("kernel_h", 2);
pool.set("stride_w", 2);
pool.set("stride_h", 2);
pool.set("pad_w", 0);
pool.set("pad_h", 0);
pool.type = "Pooling";
pool.name = "testPool";
LayerParams unpool;
unpool.set("pool_k_w", 2);
unpool.set("pool_k_h", 2);
unpool.set("pool_stride_w", 2);
unpool.set("pool_stride_h", 2);
unpool.set("pool_pad_w", 0);
unpool.set("pool_pad_h", 0);
unpool.type = "MaxUnpool";
unpool.name = "testUnpool";
Net net;
int poolId = net.addLayer(pool.name, pool.type, pool);
net.connect(0, 0, poolId, 0);
int unpoolId = net.addLayer(unpool.name, unpool.type, unpool);
net.connect(poolId, 0, unpoolId, 0);
net.connect(poolId, 1, unpoolId, 1);
Mat input({1, 1, 4, 4}, CV_32F);
randu(input, -1.0f, 1.0f);
net.setInput(input);
Mat outputDefault = net.forward("testUnpool").clone();
net.setPreferableBackend(DNN_BACKEND_HALIDE);
net.setInput(input);
Mat outputHalide = net.forward("testUnpool").clone();
normAssert(outputDefault, outputHalide);
}
////////////////////////////////////////////////////////////////////////////////
// AvePooling + in-place layers
////////////////////////////////////////////////////////////////////////////////
static const int kNumChannels = 3;
void testInPlaceActivation(LayerParams& lp)
{
EXPECT_FALSE(lp.name.empty());
LayerParams pool;
pool.set("pool", "ave");
pool.set("kernel_w", 2);
pool.set("kernel_h", 2);
pool.set("stride_w", 2);
pool.set("stride_h", 2);
pool.type = "Pooling";
Net net;
int poolId = net.addLayer(pool.name, pool.type, pool);
net.connect(0, 0, poolId, 0);
net.addLayerToPrev(lp.name, lp.type, lp);
Mat input({1, kNumChannels, 10, 10}, CV_32F);
randu(input, -1.0f, 1.0f);
net.setInput(input);
Mat outputDefault = net.forward(lp.name).clone();
net.setInput(input);
net.setPreferableBackend(DNN_BACKEND_HALIDE);
Mat outputHalide = net.forward(lp.name).clone();
normAssert(outputDefault, outputHalide);
}
typedef TestWithParam<tuple<bool, bool, float> > BatchNorm;
TEST_P(BatchNorm, Accuracy)
{
bool hasWeights = get<0>(GetParam());
bool hasBias = get<1>(GetParam());
float epsilon = get<2>(GetParam());
LayerParams lp;
lp.set("has_weight", hasWeights);
lp.set("has_bias", hasBias);
lp.set("eps", epsilon);
lp.type = "BatchNorm";
lp.name = "testLayer";
lp.blobs.reserve(4);
for (int i = 0; i < 3; ++i)
lp.blobs.push_back(Mat({kNumChannels}, CV_32F));
if (hasBias || hasWeights)
lp.blobs.push_back(Mat({kNumChannels}, CV_32F));
for (Mat& m : lp.blobs)
randu(m, 0.0f, 1.0f);
testInPlaceActivation(lp);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, BatchNorm, Combine(
/*has weights*/ Bool(),
/*has bias*/ Bool(),
/*epsilon*/ Values(1e-3f, 1e-5f)
));
typedef TestWithParam<tuple<float> > ReLU;
TEST_P(ReLU, Accuracy)
{
float negativeSlope = get<0>(GetParam());
LayerParams lp;
lp.set("negative_slope", negativeSlope);
lp.type = "ReLU";
lp.name = "testLayer";
testInPlaceActivation(lp);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, ReLU, Values(
/*negative slope*/ 2.0f, 0.3f, -0.1f, 0.0f
));
typedef TestWithParam<tuple<std::string> > NoParamActivation;
TEST_P(NoParamActivation, Accuracy)
{
LayerParams lp;
lp.type = get<0>(GetParam());
lp.name = "testLayer";
testInPlaceActivation(lp);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, NoParamActivation, Values(
/*type*/ "TanH", "Sigmoid", "AbsVal", "BNLL"
));
typedef TestWithParam<tuple<Vec3f> > Power;
TEST_P(Power, Accuracy)
{
float power = get<0>(GetParam())[0];
float scale = get<0>(GetParam())[1];
float shift = get<0>(GetParam())[2];
LayerParams lp;
lp.set("power", power);
lp.set("scale", scale);
lp.set("shift", shift);
lp.type = "Power";
lp.name = "testLayer";
testInPlaceActivation(lp);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Power,
/*power, scale, shift*/ Values(Vec3f(0.9f, 1.0f, 1.1f), Vec3f(0.9f, 1.1f, 1.0f),
Vec3f(1.0f, 0.9f, 1.1f), Vec3f(1.0f, 1.1f, 0.9f),
Vec3f(1.1f, 0.9f, 1.0f), Vec3f(1.1f, 1.0f, 0.9f))
);
TEST(ChannelsPReLU, Accuracy)
{
LayerParams lp;
lp.type = "ChannelsPReLU";
lp.name = "testLayer";
lp.blobs.push_back(Mat({kNumChannels}, CV_32F));
randu(lp.blobs[0], -1.0f, 1.0f);
testInPlaceActivation(lp);
}
typedef TestWithParam<tuple<bool> > Scale;
TEST_P(Scale, Accuracy)
{
bool hasBias = get<0>(GetParam());
LayerParams lp;
lp.set("bias_term", hasBias);
lp.type = "Scale";
lp.name = "testLayer";
lp.blobs.push_back(Mat({kNumChannels}, CV_32F));
randu(lp.blobs[0], -1.0f, 1.0f);
if (hasBias)
{
lp.blobs.push_back(Mat({kNumChannels}, CV_32F));
randu(lp.blobs[1], -1.0f, 1.0f);
}
testInPlaceActivation(lp);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Scale, Values(true, false));
////////////////////////////////////////////////////////////////////////////////
// Concat layer
////////////////////////////////////////////////////////////////////////////////
//
// input --- conv --- concat --- output
// `--- conv ----^ ^ ^
// `---- ... ------' '
// `-----------------'
typedef TestWithParam<tuple<Vec3i, Vec3i> > Concat;
TEST_P(Concat, Accuracy)
{
Vec3i inSize = get<0>(GetParam());
Vec3i numChannels = get<1>(GetParam());
Net net;
std::vector<int> convLayerIds;
convLayerIds.reserve(numChannels.channels);
for (int i = 0, n = numChannels.channels; i < n; ++i)
{
if (!numChannels[i])
break;
Mat weights({numChannels[i], inSize[0], 1, 1}, CV_32F);
randu(weights, -1.0f, 1.0f);
LayerParams convParam;
convParam.set("kernel_w", 1);
convParam.set("kernel_h", 1);
convParam.set("num_output", numChannels[i]);
convParam.set("bias_term", false);
convParam.type = "Convolution";
std::ostringstream ss;
ss << "convLayer" << i;
convParam.name = ss.str();
convParam.blobs.push_back(weights);
int layerId = net.addLayer(convParam.name, convParam.type, convParam);
convLayerIds.push_back(layerId);
net.connect(0, 0, layerId, 0);
}
LayerParams concatParam;
concatParam.type = "Concat";
concatParam.name = "testLayer";
int concatId = net.addLayer(concatParam.name, concatParam.type, concatParam);
net.connect(0, 0, concatId, 0);
for (int i = 0; i < convLayerIds.size(); ++i)
{
net.connect(convLayerIds[i], 0, concatId, i + 1);
}
Mat input({1, inSize[0], inSize[1], inSize[2]}, CV_32F);
randu(input, -1.0f, 1.0f);
net.setInput(input);
Mat outputDefault = net.forward(concatParam.name).clone();
net.setPreferableBackend(DNN_BACKEND_HALIDE);
Mat outputHalide = net.forward(concatParam.name).clone();
normAssert(outputDefault, outputHalide);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Concat, Combine(
/*input size*/ Values(Vec3i(1, 4, 5), Vec3i(2, 8, 6)),
/*channels*/ Values(Vec3i(2, 0, 0), Vec3i(3, 4, 0), Vec3i(1, 6, 2))
));
////////////////////////////////////////////////////////////////////////////////
// Element-wise layers
////////////////////////////////////////////////////////////////////////////////
//
// input --- conv --- eltwise --- output
// `--- conv ----^ ^ ^
// `---- ... ------' '
// `-----------------'
typedef TestWithParam<tuple<Vec3i, std::string, int, bool> > Eltwise;
TEST_P(Eltwise, Accuracy)
{
Vec3i inSize = get<0>(GetParam());
std::string op = get<1>(GetParam());
int numConv = get<2>(GetParam());
bool weighted = get<3>(GetParam());
Net net;
std::vector<int> convLayerIds(numConv);
for (int i = 0; i < numConv; ++i)
{
Mat weights({inSize[0], inSize[0], 1, 1}, CV_32F);
randu(weights, -1.0f, 1.0f);
LayerParams convParam;
convParam.set("kernel_w", 1);
convParam.set("kernel_h", 1);
convParam.set("num_output", inSize[0]);
convParam.set("bias_term", false);
convParam.type = "Convolution";
std::ostringstream ss;
ss << "convLayer" << i;
convParam.name = ss.str();
convParam.blobs.push_back(weights);
convLayerIds[i] = net.addLayer(convParam.name, convParam.type, convParam);
net.connect(0, 0, convLayerIds[i], 0);
}
LayerParams eltwiseParam;
eltwiseParam.set("operation", op);
if (op == "sum" && weighted)
{
RNG rng = cv::theRNG();
std::vector<float> coeff(1 + numConv);
for (int i = 0; i < coeff.size(); ++i)
{
coeff[i] = rng.uniform(-2.0f, 2.0f);
}
eltwiseParam.set("coeff", DictValue::arrayReal<float*>(&coeff[0], coeff.size()));
}
eltwiseParam.type = "Eltwise";
eltwiseParam.name = "testLayer";
int eltwiseId = net.addLayer(eltwiseParam.name, eltwiseParam.type, eltwiseParam);
net.connect(0, 0, eltwiseId, 0);
for (int i = 0; i < numConv; ++i)
{
net.connect(convLayerIds[i], 0, eltwiseId, i + 1);
}
Mat input({1, inSize[0], inSize[1], inSize[2]}, CV_32F);
randu(input, -1.0f, 1.0f);
net.setInput(input);
Mat outputDefault = net.forward(eltwiseParam.name).clone();
net.setPreferableBackend(DNN_BACKEND_HALIDE);
Mat outputHalide = net.forward(eltwiseParam.name).clone();
normAssert(outputDefault, outputHalide);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Eltwise, Combine(
/*input size*/ Values(Vec3i(1, 4, 5), Vec3i(2, 8, 6)),
/*operation*/ Values("prod", "sum", "max"),
/*num convs*/ Values(1, 2, 3),
/*weighted(for sum only)*/ Bool()
));
////////////////////////////////////////////////////////////////////////////
// Mixed backends
////////////////////////////////////////////////////////////////////////////
TEST(MixedBackends_Halide_Default_Halide, Accuracy)
{
// Just a layer that supports Halide backend.
LayerParams lrn;
lrn.type = "LRN";
lrn.name = "testLRN";
// Some of layers that doesn't supports Halide backend yet.
LayerParams mvn;
mvn.type = "MVN";
mvn.name = "testMVN";
// Halide layer again.
LayerParams lrn2;
lrn2.type = "LRN";
lrn2.name = "testLRN2";
Net net;
int lrnId = net.addLayer(lrn.name, lrn.type, lrn);
net.connect(0, 0, lrnId, 0);
net.addLayerToPrev(mvn.name, mvn.type, mvn);
net.addLayerToPrev(lrn2.name, lrn2.type, lrn2);
Mat input({4, 3, 5, 6}, CV_32F);
randu(input, -1.0f, 1.0f);
net.setInput(input);
Mat outputDefault = net.forward().clone();
net.setPreferableBackend(DNN_BACKEND_HALIDE);
net.setInput(input);
Mat outputHalide = net.forward().clone();
normAssert(outputDefault, outputHalide);
net.setPreferableTarget(DNN_TARGET_OPENCL);
net.setInput(input);
outputHalide = net.forward().clone();
normAssert(outputDefault, outputHalide);
}
#endif // HAVE_HALIDE
} // namespace cvtest
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