Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

Merge pull request #25277 from alexlyulkov:al/dnn-int-tests

Browse Source 
Added int tests for CumSum, Scatter, Tile and ReduceSum dnn layers #25277

Fixed bug in tile layer.
Fixed bug in reduce layer by reimplementing the layer. 

Fixed types filter in Scatter and ScatterND layers

PR for extra: https://github.com/opencv/opencv_extra/pull/1161


### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
pull/25335/head
alexlyulkov 8 months ago committed by GitHub
parent 87e0246bb0
commit 5144766380
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
6 changed files with 218 additions and 3 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 2
      modules/dnn/src/layers/scatterND_layer.cpp
  2. 2
      modules/dnn/src/layers/scatter_layer.cpp
  3. 2
      modules/dnn/src/layers/tile_layer.cpp
  4. 3
      modules/dnn/src/onnx/onnx_importer.cpp
  5. 193
      modules/dnn/test/test_int.cpp
  6. 19
      modules/dnn/test/test_onnx_importer.cpp

2
modules/dnn/src/layers/scatterND_layer.cpp
Unescape View File

@ -76,7 +76,7 @@ public:
std::vector<MatType>& internals) const CV_OVERRIDE
{
CV_CheckEQ(inputs.size(), (size_t)3, "");
CV_CheckType(inputs[0], inputs[0] == CV_32F || inputs[0] == CV_32S || inputs[0] == CV_16F || inputs[0] == CV_8U, "");
CV_CheckType(inputs[0], inputs[0] == CV_32F || inputs[0] == CV_32S || inputs[0] == CV_64S || inputs[0] == CV_16F || inputs[0] == CV_8U, "");
CV_CheckType(inputs[1], inputs[1] == CV_64S || inputs[1] == CV_32S, "");
CV_CheckTypeEQ(inputs[2], inputs[0], "");
outputs.assign(1, inputs[0]);

2
modules/dnn/src/layers/scatter_layer.cpp
Unescape View File

@ -70,7 +70,7 @@ public:
std::vector<MatType>& internals) const CV_OVERRIDE
{
CV_CheckEQ(inputs.size(), (size_t)3, "");
CV_CheckType(inputs[0], inputs[0] == CV_32F || inputs[0] == CV_32S || inputs[0] == CV_16F || inputs[0] == CV_8U, "");
CV_CheckType(inputs[0], inputs[0] == CV_32F || inputs[0] == CV_32S || inputs[0] == CV_64S || inputs[0] == CV_16F || inputs[0] == CV_8U, "");
CV_CheckType(inputs[1], inputs[1] == CV_64S || inputs[1] == CV_32S, "");
CV_CheckTypeEQ(inputs[2], inputs[0], "");
outputs.assign(1, inputs[0]);

2
modules/dnn/src/layers/tile_layer.cpp
Unescape View File

@ -86,8 +86,8 @@ public:
{
tmp = tmp.reshape(0, dims);
tmp = cv::repeat(tmp, 1, rep_i);
dims *= out_shape[i];
}
dims *= out_shape[i];
}
tmp = tmp.reshape(0, out_shape);

3
modules/dnn/src/onnx/onnx_importer.cpp
Unescape View File

@ -1194,6 +1194,9 @@ void ONNXImporter::parseReduce(LayerParams& layerParams, const opencv_onnx::Node
int num_inputs = node_proto.input_size();
CV_Check(num_inputs, num_inputs >= 1 && num_inputs <= 2, "DNN/ONNX: Reduce layers should have at least one input and at most two inputs");
if (num_inputs >= 2)
CV_CheckTrue(constBlobs.find(node_proto.input(1)) != constBlobs.end(), "Reduce layer doesn't support non contant axes");
// "axes" is turned to one of the inputs since opset 18,
// except for ReduceSum, which has "axes" input since opset 13.
if (!layerParams.has("axes") && num_inputs == 2 && constBlobs.find(node_proto.input(1)) != constBlobs.end()) {

193
modules/dnn/test/test_int.cpp
Unescape View File

@ -496,4 +496,197 @@ INSTANTIATE_TEST_CASE_P(/**/, Test_Flatten_Int, Combine(
dnnBackendsAndTargets()
));
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Tile_Int;
TEST_P(Test_Tile_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> repeats{1, 1, 2, 3};
Net net;
LayerParams lp;
lp.type = "Tile";
lp.name = "testLayer";
lp.set("repeats", DictValue::arrayInt<int*>(repeats.data(), repeats.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], inShape[0] * repeats[0]);
EXPECT_EQ(re.size[1], inShape[1] * repeats[1]);
EXPECT_EQ(re.size[2], inShape[2] * repeats[2]);
EXPECT_EQ(re.size[3], inShape[3] * repeats[3]);
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_Tile_Int, Combine(
testing::Values(CV_32S, CV_64S),
dnnBackendsAndTargets()
));
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Reduce_Int;
TEST_P(Test_Reduce_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{5, 4, 3, 2};
int64_t low = matType == CV_64S ? 1000000000000000ll : 100000000;
Mat input(inShape, matType);
cv::randu(input, low, low + 100);
std::vector<int> axes{1};
Net net;
LayerParams lp;
lp.type = "Reduce";
lp.name = "testLayer";
lp.set("reduce", "SUM");
lp.set("keepdims", false);
lp.set("axes", DictValue::arrayInt<int*>(axes.data(), axes.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(), 3);
EXPECT_EQ(re.size[0], inShape[0]);
EXPECT_EQ(re.size[1], inShape[2]);
EXPECT_EQ(re.size[2], inShape[3]);
std::vector<int> inIndices(4);
std::vector<int> reIndices(3);
for (int i0 = 0; i0 < re.size[0]; ++i0)
{
inIndices[0] = i0;
reIndices[0] = i0;
for (int i1 = 0; i1 < re.size[1]; ++i1)
{
inIndices[2] = i1;
reIndices[1] = i1;
for (int i2 = 0; i2 < re.size[2]; ++i2)
{
inIndices[3] = i2;
reIndices[2] = i2;
int64_t value = 0;
for (int j = 0; j < input.size[1]; ++j)
{
inIndices[1] = j;
value += getValueAt(input, inIndices.data());
}
EXPECT_EQ(getValueAt(re, reIndices.data()), value);
}
}
}
}
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Reduce_Int;
TEST_P(Test_Reduce_Int, two_axes)
{
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{5, 4, 3, 2};
int64_t low = matType == CV_64S ? 100000000000000ll : 10000000;
Mat input(inShape, matType);
cv::randu(input, low, low + 100);
std::vector<int> axes{1, 3};
Net net;
LayerParams lp;
lp.type = "Reduce";
lp.name = "testLayer";
lp.set("reduce", "SUM");
lp.set("keepdims", false);
lp.set("axes", DictValue::arrayInt<int*>(axes.data(), axes.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(), 2);
EXPECT_EQ(re.size[0], inShape[0]);
EXPECT_EQ(re.size[1], inShape[2]);
std::vector<int> inIndices(4);
std::vector<int> reIndices(2);
for (int i0 = 0; i0 < re.size[0]; ++i0)
{
inIndices[0] = i0;
reIndices[0] = i0;
for (int i1 = 0; i1 < re.size[1]; ++i1)
{
inIndices[2] = i1;
reIndices[1] = i1;
int64_t value = 0;
for (int i2 = 0; i2 < input.size[3]; ++i2)
{
inIndices[3] = i2;
for (int j = 0; j < input.size[1]; ++j)
{
inIndices[1] = j;
value += getValueAt(input, inIndices.data());
}
}
EXPECT_EQ(getValueAt(re, reIndices.data()), value);
}
}
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Reduce_Int, Combine(
testing::Values(CV_32S, CV_64S),
dnnBackendsAndTargets()
));
}} // namespace

19
modules/dnn/test/test_onnx_importer.cpp
Unescape View File

@ -2630,6 +2630,25 @@ TEST_P(Test_ONNX_layers, CumSum)
testONNXModels("cumsum_1d_exclusive_1_reverse");
testONNXModels("cumsum_2d_dim_1");
testONNXModels("cumsum_3d_dim_2");
testONNXModels("cumsum_3d_dim_2_int32");
testONNXModels("cumsum_3d_dim_2_int64");
}
TEST_P(Test_ONNX_layers, ReduceSumInt)
{
testONNXModels("reduce_sum_int64");
}
TEST_P(Test_ONNX_layers, ScatterInt)
{
testONNXModels("scatter_int32", npy, 0, 0, false, true, 3);
testONNXModels("scatter_int64", npy, 0, 0, false, true, 3);
}
TEST_P(Test_ONNX_layers, TileInt)
{
testONNXModels("tile_int32");
testONNXModels("tile_int64");
}
static void testYOLO(const std::string& weightPath, const std::vector<int>& refClassIds,

Write Preview
Loading…
Cancel
Save