Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
499 lines
15 KiB
499 lines
15 KiB
// 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. |
|
|
|
#include "test_precomp.hpp" |
|
#include <opencv2/dnn/shape_utils.hpp> |
|
|
|
namespace opencv_test { namespace { |
|
|
|
int64_t getValueAt(const Mat &m, const int *indices) |
|
{ |
|
if (m.type() == CV_32S) |
|
return m.at<int32_t>(indices); |
|
else if (m.type() == CV_64S) |
|
return m.at<int64_t>(indices); |
|
else |
|
CV_Error(Error::BadDepth, "Unsupported type"); |
|
return -1; |
|
} |
|
|
|
typedef testing::TestWithParam<tuple<Backend, Target> > Test_int64_sum; |
|
TEST_P(Test_int64_sum, basic) |
|
{ |
|
Backend backend = get<0>(GetParam()); |
|
Target target = get<1>(GetParam()); |
|
|
|
int64_t a_value = 1000000000000000ll; |
|
int64_t b_value = 1; |
|
int64_t result_value = 1000000000000001ll; |
|
EXPECT_NE(int64_t(float(a_value) + float(b_value)), result_value); |
|
|
|
Mat a(3, 5, CV_64SC1, cv::Scalar_<int64_t>(a_value)); |
|
Mat b = Mat::ones(3, 5, CV_64S); |
|
|
|
Net net; |
|
LayerParams lp; |
|
lp.type = "NaryEltwise"; |
|
lp.name = "testLayer"; |
|
lp.set("operation", "sum"); |
|
int id = net.addLayerToPrev(lp.name, lp.type, lp); |
|
net.connect(0, 1, id, 1); |
|
|
|
vector<String> inpNames(2); |
|
inpNames[0] = "a"; |
|
inpNames[1] = "b"; |
|
net.setInputsNames(inpNames); |
|
net.setInput(a, inpNames[0]); |
|
net.setInput(b, inpNames[1]); |
|
|
|
net.setPreferableBackend(backend); |
|
net.setPreferableTarget(target); |
|
|
|
Mat re; |
|
re = net.forward(); |
|
EXPECT_EQ(re.depth(), CV_64S); |
|
auto ptr_re = (int64_t *) re.data; |
|
for (int i = 0; i < re.total(); i++) |
|
ASSERT_EQ(result_value, ptr_re[i]); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_int64_sum, |
|
dnnBackendsAndTargets() |
|
); |
|
|
|
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Expand_Int; |
|
TEST_P(Test_Expand_Int, random) |
|
{ |
|
int matType = get<0>(GetParam()); |
|
tuple<Backend, Target> backend_target= get<1>(GetParam()); |
|
Backend backend = get<0>(backend_target); |
|
Target target = get<1>(backend_target); |
|
|
|
std::vector<int> inShape{2, 3, 1, 5}; |
|
int64_t low = matType == CV_64S ? 1000000000000000ll : 1000000000; |
|
Mat input(inShape, matType); |
|
cv::randu(input, low, low + 100); |
|
std::vector<int> outShape{2, 1, 4, 5}; |
|
|
|
Net net; |
|
LayerParams lp; |
|
lp.type = "Expand"; |
|
lp.name = "testLayer"; |
|
lp.set("shape", DictValue::arrayInt<int*>(&outShape[0], outShape.size())); |
|
net.addLayerToPrev(lp.name, lp.type, lp); |
|
|
|
net.setInput(input); |
|
net.setPreferableBackend(backend); |
|
net.setPreferableTarget(target); |
|
|
|
Mat re; |
|
re = net.forward(); |
|
EXPECT_EQ(re.depth(), matType); |
|
EXPECT_EQ(re.size.dims(), 4); |
|
EXPECT_EQ(re.size[0], 2); |
|
EXPECT_EQ(re.size[1], 3); |
|
EXPECT_EQ(re.size[2], 4); |
|
EXPECT_EQ(re.size[3], 5); |
|
|
|
std::vector<int> inIndices(4); |
|
std::vector<int> reIndices(4); |
|
for (int i0 = 0; i0 < re.size[0]; ++i0) |
|
{ |
|
inIndices[0] = i0 % inShape[0]; |
|
reIndices[0] = i0; |
|
for (int i1 = 0; i1 < re.size[1]; ++i1) |
|
{ |
|
inIndices[1] = i1 % inShape[1]; |
|
reIndices[1] = i1; |
|
for (int i2 = 0; i2 < re.size[2]; ++i2) |
|
{ |
|
inIndices[2] = i2 % inShape[2]; |
|
reIndices[2] = i2; |
|
for (int i3 = 0; i3 < re.size[3]; ++i3) |
|
{ |
|
inIndices[3] = i3 % inShape[3]; |
|
reIndices[3] = i3; |
|
EXPECT_EQ(getValueAt(re, reIndices.data()), getValueAt(input, inIndices.data())); |
|
} |
|
} |
|
} |
|
} |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Expand_Int, Combine( |
|
testing::Values(CV_32S, CV_64S), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Permute_Int; |
|
TEST_P(Test_Permute_Int, random) |
|
{ |
|
int matType = get<0>(GetParam()); |
|
tuple<Backend, Target> backend_target= get<1>(GetParam()); |
|
Backend backend = get<0>(backend_target); |
|
Target target = get<1>(backend_target); |
|
|
|
std::vector<int> inShape{2, 3, 4, 5}; |
|
int64_t low = matType == CV_64S ? 1000000000000000ll : 1000000000; |
|
Mat input(inShape, matType); |
|
cv::randu(input, low, low + 100); |
|
std::vector<int> order{0, 2, 3, 1}; |
|
|
|
Net net; |
|
LayerParams lp; |
|
lp.type = "Permute"; |
|
lp.name = "testLayer"; |
|
lp.set("order", DictValue::arrayInt<int*>(&order[0], order.size())); |
|
net.addLayerToPrev(lp.name, lp.type, lp); |
|
|
|
net.setInput(input); |
|
net.setPreferableBackend(backend); |
|
net.setPreferableTarget(target); |
|
|
|
Mat re; |
|
re = net.forward(); |
|
EXPECT_EQ(re.depth(), matType); |
|
EXPECT_EQ(re.size.dims(), 4); |
|
EXPECT_EQ(re.size[0], 2); |
|
EXPECT_EQ(re.size[1], 4); |
|
EXPECT_EQ(re.size[2], 5); |
|
EXPECT_EQ(re.size[3], 3); |
|
|
|
std::vector<int> inIndices(4); |
|
std::vector<int> reIndices(4); |
|
for (int i0 = 0; i0 < input.size[0]; ++i0) |
|
{ |
|
inIndices[0] = i0; |
|
reIndices[0] = i0; |
|
for (int i1 = 0; i1 < input.size[1]; ++i1) |
|
{ |
|
inIndices[1] = i1; |
|
reIndices[3] = i1; |
|
for (int i2 = 0; i2 < input.size[2]; ++i2) |
|
{ |
|
inIndices[2] = i2; |
|
reIndices[1] = i2; |
|
for (int i3 = 0; i3 < input.size[3]; ++i3) |
|
{ |
|
inIndices[3] = i3; |
|
reIndices[2] = i3; |
|
EXPECT_EQ(getValueAt(re, reIndices.data()), getValueAt(input, inIndices.data())); |
|
} |
|
} |
|
} |
|
} |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Permute_Int, Combine( |
|
testing::Values(CV_32S, CV_64S), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
typedef testing::TestWithParam<tuple<int, int, tuple<Backend, Target> > > Test_GatherElements_Int; |
|
TEST_P(Test_GatherElements_Int, random) |
|
{ |
|
int matType = get<0>(GetParam()); |
|
int indicesType = get<1>(GetParam()); |
|
tuple<Backend, Target> backend_target= get<2>(GetParam()); |
|
Backend backend = get<0>(backend_target); |
|
Target target = get<1>(backend_target); |
|
|
|
std::vector<int> inShape{2, 3, 4, 5}; |
|
int64_t low = matType == CV_64S ? 1000000000000000ll : 1000000000; |
|
Mat input(inShape, matType); |
|
cv::randu(input, low, low + 100); |
|
|
|
std::vector<int> indicesShape{2, 3, 10, 5}; |
|
Mat indicesMat(indicesShape, indicesType); |
|
cv::randu(indicesMat, 0, 4); |
|
|
|
Net net; |
|
LayerParams lp; |
|
lp.type = "GatherElements"; |
|
lp.name = "testLayer"; |
|
lp.set("axis", 2); |
|
int id = net.addLayerToPrev(lp.name, lp.type, lp); |
|
net.connect(0, 1, id, 1); |
|
|
|
std::vector<String> inpNames(2); |
|
inpNames[0] = "gather_input"; |
|
inpNames[1] = "gather_indices"; |
|
net.setInputsNames(inpNames); |
|
net.setInput(input, inpNames[0]); |
|
net.setInput(indicesMat, inpNames[1]); |
|
|
|
net.setPreferableBackend(backend); |
|
net.setPreferableTarget(target); |
|
|
|
Mat re; |
|
re = net.forward(); |
|
EXPECT_EQ(re.depth(), matType); |
|
EXPECT_EQ(re.size.dims(), 4); |
|
ASSERT_EQ(shape(indicesMat), shape(re)); |
|
|
|
std::vector<int> inIndices(4); |
|
std::vector<int> reIndices(4); |
|
for (int i0 = 0; i0 < input.size[0]; ++i0) |
|
{ |
|
inIndices[0] = i0; |
|
reIndices[0] = i0; |
|
for (int i1 = 0; i1 < input.size[1]; ++i1) |
|
{ |
|
inIndices[1] = i1; |
|
reIndices[1] = i1; |
|
for (int i2 = 0; i2 < indicesMat.size[2]; ++i2) |
|
{ |
|
reIndices[2] = i2; |
|
for (int i3 = 0; i3 < input.size[3]; ++i3) |
|
{ |
|
inIndices[3] = i3; |
|
reIndices[3] = i3; |
|
inIndices[2] = getValueAt(indicesMat, reIndices.data()); |
|
EXPECT_EQ(getValueAt(re, reIndices.data()), getValueAt(input, inIndices.data())); |
|
} |
|
} |
|
} |
|
} |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_GatherElements_Int, Combine( |
|
testing::Values(CV_32S, CV_64S), |
|
testing::Values(CV_32S, CV_64S), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
typedef testing::TestWithParam<tuple<int, int, tuple<Backend, Target> > > Test_Gather_Int; |
|
TEST_P(Test_Gather_Int, random) |
|
{ |
|
int matType = get<0>(GetParam()); |
|
int indicesType = get<1>(GetParam()); |
|
tuple<Backend, Target> backend_target= get<2>(GetParam()); |
|
Backend backend = get<0>(backend_target); |
|
Target target = get<1>(backend_target); |
|
|
|
std::vector<int> inShape{5, 1}; |
|
int64_t low = matType == CV_64S ? 1000000000000000ll : 1000000000; |
|
Mat input(inShape, matType); |
|
cv::randu(input, low, low + 100); |
|
|
|
std::vector<int> indices_shape = {1, 1}; |
|
Mat indicesMat = cv::Mat(indices_shape, indicesType, 0.0); |
|
|
|
std::vector<int> output_shape = {5, 1}; |
|
cv::Mat outputRef = cv::Mat(output_shape, matType, input(cv::Range::all(), cv::Range(0, 1)).data); |
|
|
|
Net net; |
|
LayerParams lp; |
|
lp.type = "Gather"; |
|
lp.name = "testLayer"; |
|
lp.set("axis", 1); |
|
lp.set("real_ndims", 1); |
|
int id = net.addLayerToPrev(lp.name, lp.type, lp); |
|
net.connect(0, 1, id, 1); |
|
|
|
std::vector<String> inpNames(2); |
|
inpNames[0] = "gather_input"; |
|
inpNames[1] = "gather_indices"; |
|
net.setInputsNames(inpNames); |
|
net.setInput(input, inpNames[0]); |
|
net.setInput(indicesMat, inpNames[1]); |
|
|
|
net.setPreferableBackend(backend); |
|
net.setPreferableTarget(target); |
|
|
|
Mat re; |
|
re = net.forward(); |
|
EXPECT_EQ(re.depth(), matType); |
|
|
|
ASSERT_EQ(shape(outputRef), shape(re)); |
|
normAssert(outputRef, re); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Gather_Int, Combine( |
|
testing::Values(CV_32S, CV_64S), |
|
testing::Values(CV_32S, CV_64S), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
typedef testing::TestWithParam<tuple<int, int, tuple<Backend, Target> > > Test_Cast_Int; |
|
TEST_P(Test_Cast_Int, random) |
|
{ |
|
int inMatType = get<0>(GetParam()); |
|
int outMatType = get<1>(GetParam()); |
|
tuple<Backend, Target> backend_target= get<2>(GetParam()); |
|
Backend backend = get<0>(backend_target); |
|
Target target = get<1>(backend_target); |
|
|
|
std::vector<int> inShape{2, 3, 4, 5}; |
|
Mat input(inShape, inMatType); |
|
cv::randu(input, 200, 300); |
|
Mat outputRef; |
|
input.convertTo(outputRef, outMatType); |
|
|
|
Net net; |
|
LayerParams lp; |
|
lp.type = "Cast"; |
|
lp.name = "testLayer"; |
|
lp.set("outputType", outMatType); |
|
net.addLayerToPrev(lp.name, lp.type, lp); |
|
|
|
net.setInput(input); |
|
net.setPreferableBackend(backend); |
|
net.setPreferableTarget(target); |
|
|
|
Mat re; |
|
re = net.forward(); |
|
EXPECT_EQ(re.depth(), outMatType); |
|
EXPECT_EQ(re.size.dims(), 4); |
|
|
|
ASSERT_EQ(shape(input), shape(re)); |
|
normAssert(outputRef, re); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Cast_Int, Combine( |
|
testing::Values(CV_32S, CV_64S), |
|
testing::Values(CV_32S, CV_64S), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Slice_Int; |
|
TEST_P(Test_Slice_Int, random) |
|
{ |
|
int matType = get<0>(GetParam()); |
|
tuple<Backend, Target> backend_target= get<1>(GetParam()); |
|
Backend backend = get<0>(backend_target); |
|
Target target = get<1>(backend_target); |
|
|
|
std::vector<int> inputShape{1, 16, 6, 8}; |
|
std::vector<int> begin{0, 4, 0, 0}; |
|
std::vector<int> end{1, 8, 6, 8}; |
|
int64_t low = matType == CV_64S ? 1000000000000000ll : 1000000000; |
|
Mat input(inputShape, matType); |
|
cv::randu(input, low, low + 100); |
|
|
|
std::vector<Range> range(4); |
|
for (int i = 0; i < 4; ++i) |
|
range[i] = Range(begin[i], end[i]); |
|
|
|
Net net; |
|
LayerParams lp; |
|
lp.type = "Slice"; |
|
lp.name = "testLayer"; |
|
lp.set("begin", DictValue::arrayInt<int*>(&(begin[0]), 4)); |
|
lp.set("end", DictValue::arrayInt<int*>(&(end[0]), 4)); |
|
net.addLayerToPrev(lp.name, lp.type, lp); |
|
|
|
net.setInput(input); |
|
net.setPreferableBackend(backend); |
|
net.setPreferableTarget(target); |
|
Mat out = net.forward(); |
|
|
|
EXPECT_GT(cv::norm(out, NORM_INF), 0); |
|
normAssert(out, input(range)); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Slice_Int, Combine( |
|
testing::Values(CV_32S, CV_64S), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Reshape_Int; |
|
TEST_P(Test_Reshape_Int, random) |
|
{ |
|
int matType = get<0>(GetParam()); |
|
tuple<Backend, Target> backend_target= get<1>(GetParam()); |
|
Backend backend = get<0>(backend_target); |
|
Target target = get<1>(backend_target); |
|
|
|
std::vector<int> inShape{2, 3, 4, 5}; |
|
std::vector<int> outShape{2, 3, 2, 10}; |
|
int64_t low = matType == CV_64S ? 1000000000000000ll : 1000000000; |
|
Mat input(inShape, matType); |
|
cv::randu(input, low, low + 100); |
|
|
|
Net net; |
|
LayerParams lp; |
|
lp.type = "Reshape"; |
|
lp.name = "testLayer"; |
|
lp.set("dim", DictValue::arrayInt<int*>(&outShape[0], outShape.size())); |
|
net.addLayerToPrev(lp.name, lp.type, lp); |
|
|
|
net.setInput(input); |
|
net.setPreferableBackend(backend); |
|
net.setPreferableTarget(target); |
|
|
|
Mat re; |
|
re = net.forward(); |
|
EXPECT_EQ(re.depth(), matType); |
|
EXPECT_EQ(re.size.dims(), 4); |
|
EXPECT_EQ(re.size[0], outShape[0]); |
|
EXPECT_EQ(re.size[1], outShape[1]); |
|
EXPECT_EQ(re.size[2], outShape[2]); |
|
EXPECT_EQ(re.size[3], outShape[3]); |
|
|
|
for (int i = 0; i < input.total(); ++i) |
|
{ |
|
if (matType == CV_32S) { |
|
EXPECT_EQ(re.ptr<int32_t>()[i], input.ptr<int32_t>()[i]); |
|
} else { |
|
EXPECT_EQ(re.ptr<int64_t>()[i], input.ptr<int64_t>()[i]); |
|
} |
|
} |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Reshape_Int, Combine( |
|
testing::Values(CV_32S, CV_64S), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Flatten_Int; |
|
TEST_P(Test_Flatten_Int, random) |
|
{ |
|
int matType = get<0>(GetParam()); |
|
tuple<Backend, Target> backend_target= get<1>(GetParam()); |
|
Backend backend = get<0>(backend_target); |
|
Target target = get<1>(backend_target); |
|
|
|
std::vector<int> inShape{2, 3, 4, 5}; |
|
int64_t low = matType == CV_64S ? 1000000000000000ll : 1000000000; |
|
Mat input(inShape, matType); |
|
cv::randu(input, low, low + 100); |
|
|
|
Net net; |
|
LayerParams lp; |
|
lp.type = "Flatten"; |
|
lp.name = "testLayer"; |
|
lp.set("axis", 1); |
|
net.addLayerToPrev(lp.name, lp.type, lp); |
|
|
|
net.setInput(input); |
|
net.setPreferableBackend(backend); |
|
net.setPreferableTarget(target); |
|
|
|
Mat re; |
|
re = net.forward(); |
|
EXPECT_EQ(re.depth(), matType); |
|
EXPECT_EQ(re.size.dims(), 2); |
|
EXPECT_EQ(re.size[0], inShape[0]); |
|
EXPECT_EQ(re.size[1], inShape[1] * inShape[2] * inShape[3]); |
|
|
|
for (int i = 0; i < input.total(); ++i) |
|
{ |
|
if (matType == CV_32S) { |
|
EXPECT_EQ(re.ptr<int32_t>()[i], input.ptr<int32_t>()[i]); |
|
} else { |
|
EXPECT_EQ(re.ptr<int64_t>()[i], input.ptr<int64_t>()[i]); |
|
} |
|
} |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Flatten_Int, Combine( |
|
testing::Values(CV_32S, CV_64S), |
|
dnnBackendsAndTargets() |
|
)); |
|
|
|
}} // namespace
|
|
|