// 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 opencv_test { namespace {
using namespace cv;
using namespace cv::dnn;
using namespace testing;
static void test(Mat& input, Net& net, int backendId, int targetId)
{
DNNTestLayer::checkBackend(backendId, targetId);
randu(input, -1.0f, 1.0f);
net.setInput(input);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat outputDefault = net.forward().clone();
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat outputHalide = net.forward().clone();
double l1, lInf;
DNNTestLayer::getDefaultThresholds(backendId, targetId, &l1, &lInf);
normAssert(outputDefault, outputHalide, "", l1, lInf);
}
static void test(LayerParams& params, Mat& input, int backendId, int targetId)
{
Net net;
net.addLayerToPrev(params.name, params.type, params);
test(input, net, backendId, targetId);
}
static testing::internal::ParamGenerator dnnBackendsAndTargetsWithHalide()
{
static const tuple testCases[] = {
#ifdef HAVE_HALIDE
tuple(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),
tuple(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL),
#endif
#ifdef HAVE_INF_ENGINE
tuple(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
tuple(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
tuple(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
tuple(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
#endif
tuple(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL),
tuple(DNN_BACKEND_OPENCV, DNN_TARGET_OPENCL_FP16)
};
return testing::ValuesIn(testCases);
}
class Test_Halide_layers : public DNNTestLayer {};
////////////////////////////////////////////////////////////////////////////////
// Padding
////////////////////////////////////////////////////////////////////////////////
TEST_P(Test_Halide_layers, Padding)
{
static const int kNumRuns = 10;
std::vector paddings(8);
cv::RNG& rng = cv::theRNG();
for (int t = 0; t < kNumRuns; ++t)
{
for (int i = 0; i < paddings.size(); ++i)
paddings[i] = rng(5);
LayerParams lp;
lp.set("paddings", DictValue::arrayInt(&paddings[0], paddings.size()));
lp.type = "Padding";
lp.name = "testLayer";
int sz[] = {1 + (int)rng(10), 1 + (int)rng(10), 1 + (int)rng(10), 1 + (int)rng(10)};
Mat input(4, &sz[0], CV_32F);
test(lp, input, backend, target);
}
}
////////////////////////////////////////////////////////////////////////////////
// Convolution
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam Convolution;
TEST_P(Convolution, Accuracy)
{
int inChannels = get(GetParam())[0];
int outChannels = get(GetParam())[1];
int group = get(GetParam())[2];
Size inSize = get(GetParam());
Size kernel = get(GetParam());
Size stride = get(GetParam());
Size pad = get(GetParam());
Size dilation = get(GetParam());
bool hasBias = get(GetParam());
int backendId = get(get(GetParam()));
int targetId = get(get(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD)
throw SkipTestException("");
// TODO: unstable test cases
if (backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16) &&
inChannels == 6 && outChannels == 9 && group == 1 && inSize == Size(5, 6) &&
kernel == Size(3, 1) && stride == Size(1, 1) && pad == Size(0, 1) && dilation == Size(1, 1) &&
hasBias)
throw SkipTestException("");
int sz[] = {outChannels, inChannels / group, kernel.height, kernel.width};
Mat weights(4, &sz[0], 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(1, outChannels, CV_32F);
randu(bias, -1.0f, 1.0f);
lp.blobs.push_back(bias);
}
int inpSz[] = {1, inChannels, inSize.height, inSize.width};
Mat input(4, &inpSz[0], CV_32F);
test(lp, input, backendId, targetId);
}
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(),
dnnBackendsAndTargetsWithHalide()
));
////////////////////////////////////////////////////////////////////////////////
// Deconvolution
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam Deconvolution;
TEST_P(Deconvolution, Accuracy)
{
int inChannels = get(GetParam())[0];
int outChannels = get(GetParam())[1];
int group = get(GetParam())[2];
Size inSize = get(GetParam());
Size kernel = get(GetParam());
Size pad = get(GetParam());
Size dilation = get(GetParam());
Size stride = Size(get(GetParam())[0], get(GetParam())[1]);
Size adjPad = Size(get(GetParam())[2], get(GetParam())[3]);
bool hasBias = get(GetParam());
int backendId = get(get(GetParam()));
int targetId = get(get(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_CPU &&
dilation.width == 2 && dilation.height == 2)
throw SkipTestException("");
int sz[] = {inChannels, outChannels / group, kernel.height, kernel.width};
Mat weights(4, &sz[0], 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(1, outChannels, CV_32F);
randu(bias, -1.0f, 1.0f);
lp.blobs.push_back(bias);
}
int inpSz[] = {1, inChannels, inSize.height, inSize.width};
Mat input(4, &inpSz[0], CV_32F);
test(lp, input, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Deconvolution, Combine(
/*in channels, out channels, group*/
Values(Vec3i(6, 4, 1), Vec3i(6, 9, 3)),
/*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(),
dnnBackendsAndTargetsWithHalide()
));
////////////////////////////////////////////////////////////////////////////////
// LRN
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam LRN;
TEST_P(LRN, Accuracy)
{
int inChannels = get(GetParam())[0];
Size inSize = Size(get(GetParam())[1], get(GetParam())[2]);
int localSize = get(GetParam());
float alpha = get(GetParam())[0];
float beta = get(GetParam())[1];
float bias = get(GetParam())[2];
bool normBySize = get(GetParam());
std::string nrmType = get(GetParam());
int backendId = get(get(GetParam()));
int targetId = get(get(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
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";
int sz[] = {1, inChannels, inSize.height, inSize.width};
Mat input(4, &sz[0], CV_32F);
test(lp, input, backendId, targetId);
}
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"),
dnnBackendsAndTargetsWithHalide()
));
////////////////////////////////////////////////////////////////////////////////
// Average pooling
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam AvePooling;
TEST_P(AvePooling, Accuracy)
{
int inChannels = get(GetParam());
Size outSize = get(GetParam());; // Input size will be computed from parameters.
Size kernel = get(GetParam());
Size stride = get(GetParam());
int backendId = get(get(GetParam()));
int targetId = get(get(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD)
throw SkipTestException("");
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";
int sz[] = {1, inChannels, inHeight, inWidth};
Mat input(4, &sz[0], CV_32F);
test(lp, input, backendId, targetId);
}
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)),
dnnBackendsAndTargetsWithHalide()
));
////////////////////////////////////////////////////////////////////////////////
// Maximum pooling
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam MaxPooling;
TEST_P(MaxPooling, Accuracy)
{
int inChannels = get(GetParam());
Size inSize = get(GetParam());
Size kernel = get(GetParam());
Size stride = get(GetParam());
Size pad = get(GetParam());
int backendId = get(get(GetParam()));
int targetId = get(get(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";
int sz[] = {1, inChannels, inSize.height, inSize.width};
Mat input(4, &sz[0], CV_32F);
test(lp, input, backendId, targetId);
}
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)),
dnnBackendsAndTargetsWithHalide()
));
////////////////////////////////////////////////////////////////////////////////
// Fully-connected
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam FullyConnected;
TEST_P(FullyConnected, Accuracy)
{
int inChannels = get(GetParam());
Size inSize = get(GetParam());
int outChannels = get(GetParam());
bool hasBias = get(GetParam());
int backendId = get(get(GetParam()));
int targetId = get(get(GetParam()));
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
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";
int sz[] = {1, inChannels, inSize.height, inSize.width};
Mat input(4, &sz[0], CV_32F);
test(lp, input, backendId, targetId);
}
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(),
dnnBackendsAndTargetsWithHalide()
));
////////////////////////////////////////////////////////////////////////////////
// SoftMax
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam SoftMax;
TEST_P(SoftMax, Accuracy)
{
int inChannels = get(GetParam());
int backendId = get(get(GetParam()));
int targetId = get(get(GetParam()));
LayerParams lp;
lp.type = "SoftMax";
lp.name = "testLayer";
int sz[] = {1, inChannels, 1, 1};
Mat input(4, &sz[0], CV_32F);
test(lp, input, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, SoftMax, Combine(
Values(3, 4, 5, 1024),
dnnBackendsAndTargetsWithHalide()
));
//////////////////////////////////////////////////////////////////////////////
// Max pooling - unpooling
//////////////////////////////////////////////////////////////////////////////
TEST_P(Test_Halide_layers, MaxPoolUnpool)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
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);
int sz[] = {1, 1, 4, 4};
Mat input(4, &sz[0], CV_32F);
test(input, net, backend, target);
}
////////////////////////////////////////////////////////////////////////////////
// AvePooling + in-place layers
////////////////////////////////////////////////////////////////////////////////
static const int kNumChannels = 3;
void testInPlaceActivation(LayerParams& lp, int backendId, int targetId)
{
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);
int sz[] = {1, kNumChannels, 10, 10};
Mat input(4, &sz[0], CV_32F);
test(input, net, backendId, targetId);
}
typedef TestWithParam BatchNorm;
TEST_P(BatchNorm, Accuracy)
{
bool hasWeights = get(GetParam());
bool hasBias = get(GetParam());
float epsilon = get(GetParam());
int backendId = get(get(GetParam()));
int targetId = get(get(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(1, kNumChannels, CV_32F));
if (hasBias || hasWeights)
lp.blobs.push_back(Mat(1, kNumChannels, CV_32F));
for (int i = 0; i < lp.blobs.size(); ++i)
randu(lp.blobs[i], 0.0f, 1.0f);
testInPlaceActivation(lp, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, BatchNorm, Combine(
/*has weights*/ Bool(),
/*has bias*/ Bool(),
/*epsilon*/ Values(1e-3f, 1e-5f),
dnnBackendsAndTargetsWithHalide()
));
typedef TestWithParam ReLU;
TEST_P(ReLU, Accuracy)
{
float negativeSlope = get(GetParam());
int backendId = get(get(GetParam()));
int targetId = get(get(GetParam()));
LayerParams lp;
lp.set("negative_slope", negativeSlope);
lp.type = "ReLU";
lp.name = "testLayer";
testInPlaceActivation(lp, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, ReLU, Combine(
/*negative slope*/ Values(2.0f, 0.3f, -0.1f, 0.0f),
dnnBackendsAndTargetsWithHalide()
));
typedef TestWithParam NoParamActivation;
TEST_P(NoParamActivation, Accuracy)
{
int backendId = get(get(GetParam()));
int targetId = get(get(GetParam()));
LayerParams lp;
lp.type = get(GetParam());
lp.name = "testLayer";
testInPlaceActivation(lp, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, NoParamActivation, Combine(
/*type*/ Values("TanH", "Sigmoid", "AbsVal", "BNLL"),
dnnBackendsAndTargetsWithHalide()
));
typedef TestWithParam Power;
TEST_P(Power, Accuracy)
{
float power = get(GetParam())[0];
float scale = get(GetParam())[1];
float shift = get(GetParam())[2];
int backendId = get(get(GetParam()));
int targetId = get(get(GetParam()));
LayerParams lp;
lp.set("power", power);
lp.set("scale", scale);
lp.set("shift", shift);
lp.type = "Power";
lp.name = "testLayer";
testInPlaceActivation(lp, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Power, Combine(
/*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)),
dnnBackendsAndTargetsWithHalide()
));
TEST_P(Test_Halide_layers, ChannelsPReLU)
{
LayerParams lp;
lp.type = "ChannelsPReLU";
lp.name = "testLayer";
lp.blobs.push_back(Mat(1, kNumChannels, CV_32F));
randu(lp.blobs[0], -1.0f, 1.0f);
testInPlaceActivation(lp, backend, target);
}
typedef TestWithParam Scale;
TEST_P(Scale, Accuracy)
{
bool hasBias = get(GetParam());
int backendId = get(get(GetParam()));
int targetId = get(get(GetParam()));
LayerParams lp;
lp.set("bias_term", hasBias);
lp.type = "Scale";
lp.name = "testLayer";
lp.blobs.push_back(Mat(1, kNumChannels, CV_32F));
randu(lp.blobs[0], -1.0f, 1.0f);
if (hasBias)
{
lp.blobs.push_back(Mat(1, kNumChannels, CV_32F));
randu(lp.blobs[1], -1.0f, 1.0f);
}
testInPlaceActivation(lp, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Scale, Combine(
Bool(),
dnnBackendsAndTargetsWithHalide()
));
////////////////////////////////////////////////////////////////////////////////
// Concat layer
////////////////////////////////////////////////////////////////////////////////
//
// input --- conv --- concat --- output
// `--- conv ----^ ^ ^
// `---- ... ------' '
// `-----------------'
typedef TestWithParam Concat;
TEST_P(Concat, Accuracy)
{
Vec3i inSize = get(GetParam());
Vec3i numChannels = get(GetParam());
int backendId = get(get(GetParam()));
int targetId = get(get(GetParam()));
Net net;
std::vector convLayerIds;
convLayerIds.reserve(numChannels.channels);
for (int i = 0, n = numChannels.channels; i < n; ++i)
{
if (!numChannels[i])
break;
int sz[] = {numChannels[i], inSize[0], 1, 1};
Mat weights(4, &sz[0], 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