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Merge pull request #27656 from abhishek-gola:size_layer_add

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Added Size layer to new DNN engine #27656

Merge with https://github.com/opencv/opencv_extra/pull/1274

### 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
- [x] 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
This commit is contained in:
Abhishek Gola
2025-08-12 17:08:21 +05:30
committed by GitHub
parent 120a46b0d1
commit 38a72d57c1
8 changed files with 148 additions and 10 deletions
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modules/dnn/test/test_layers.cpp
+53
View File
@@ -2853,4 +2853,57 @@ TEST(Layer_If, resize)
}
}
TEST(Layer_Size, onnx_1d)
{
auto engine_forced = static_cast<cv::dnn::EngineType>(
cv::utils::getConfigurationParameterSizeT("OPENCV_FORCE_DNN_ENGINE", cv::dnn::ENGINE_AUTO));
if (engine_forced == cv::dnn::ENGINE_CLASSIC)
{
applyTestTag(CV_TEST_TAG_DNN_SKIP_PARSER);
return;
}
const std::string modelname = findDataFile("dnn/onnx/models/test_size_1d_model.onnx", true);
cv::dnn::Net net = cv::dnn::readNetFromONNX(modelname, ENGINE_NEW);
int sz1d[1] = {7};
cv::Mat x(1, sz1d, CV_32F);
cv::randu(x, 0, 1);
net.setInput(x);
std::vector<cv::Mat> outs;
net.forward(outs);
ASSERT_EQ(outs.size(), 1u);
EXPECT_EQ(outs[0].total(), (size_t)1);
EXPECT_EQ(outs[0].type(), CV_64S);
EXPECT_EQ(outs[0].at<int64_t>(0), static_cast<int64_t>(sz1d[0]));
}
TEST(Layer_Size, onnx_0d_scalar)
{
auto engine_forced = static_cast<cv::dnn::EngineType>(
cv::utils::getConfigurationParameterSizeT("OPENCV_FORCE_DNN_ENGINE", cv::dnn::ENGINE_AUTO));
if (engine_forced == cv::dnn::ENGINE_CLASSIC)
{
applyTestTag(CV_TEST_TAG_DNN_SKIP_PARSER);
return;
}
const std::string modelname = findDataFile("dnn/onnx/models/test_size_0d_model.onnx", true);
cv::dnn::Net net = cv::dnn::readNetFromONNX(modelname, ENGINE_NEW);
cv::Mat x(1, 1, CV_32F);
x.at<float>(0, 0) = 3.14f;
net.setInput(x);
std::vector<cv::Mat> outs;
net.forward(outs);
ASSERT_EQ(outs.size(), 1u);
EXPECT_EQ(outs[0].total(), (size_t)1);
EXPECT_EQ(outs[0].type(), CV_64S);
EXPECT_EQ(outs[0].at<int64_t>(0), 1);
}
}} // namespace