mirror of
https://github.com/opencv/opencv.git
synced 2024-12-11 22:59:16 +08:00
e63d628677
* make test more reproducible
652 lines
20 KiB
C++
652 lines
20 KiB
C++
// 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()
|
|
));
|
|
#endif // HAVE_HALIDE
|
|
|
|
} // namespace cvtest
|