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Merge pull request #24039 from dkurt:tflite_test_backends

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TFLite models on different backends (tests and improvements) #24039

### Pull Request Readiness Checklist

* MaxUnpooling with OpenVINO
* Fully connected with transposed inputs/weights with OpenVINO
* Enable backends tests for TFLite (related to https://github.com/opencv/opencv/issues/23992#issuecomment-1640691722)
* Increase existing tests thresholds

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:
Dmitry Kurtaev
2023-08-04 11:28:51 +03:00
committed by GitHub
parent 96f23e3da1
commit 4b8aeb1129
12 changed files with 211 additions and 67 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/test/test_darknet_importer.cpp
+13 -5
View File
@@ -102,11 +102,14 @@ TEST(Test_Darknet, read_yolo_voc_stream)
class Test_Darknet_layers : public DNNTestLayer
{
public:
void testDarknetLayer(const std::string& name, bool hasWeights = false, bool testBatchProcessing = true)
void testDarknetLayer(const std::string& name, bool hasWeights = false, bool testBatchProcessing = true,
double l1 = 0.0, double lInf = 0.0)
{
SCOPED_TRACE(name);
Mat inp = blobFromNPY(findDataFile("dnn/darknet/" + name + "_in.npy"));
Mat ref = blobFromNPY(findDataFile("dnn/darknet/" + name + "_out.npy"));
l1 = l1 ? l1 : default_l1;
lInf = lInf ? lInf : default_lInf;
std::string cfg = findDataFile("dnn/darknet/" + name + ".cfg");
std::string model = "";
@@ -120,7 +123,7 @@ public:
net.setPreferableTarget(target);
net.setInput(inp);
Mat out = net.forward();
normAssert(out, ref, "", default_l1, default_lInf);
normAssert(out, ref, "", l1, lInf);
if (inp.size[0] == 1 && testBatchProcessing) // test handling of batch size
{
@@ -166,8 +169,8 @@ public:
}*/
ASSERT_EQ(out2.dims, ref2.dims) << ref.dims;
normAssert(out2(ranges0), ref2, "", default_l1, default_lInf);
normAssert(out2(ranges1), ref2, "", default_l1, default_lInf);
normAssert(out2(ranges0), ref2, "", l1, lInf);
normAssert(out2(ranges1), ref2, "", l1, lInf);
}
}
};
@@ -1116,7 +1119,12 @@ TEST_P(Test_Darknet_layers, connected)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_CPU_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CPU_FP16);
testDarknetLayer("connected", true);
double l1 = 0.0;
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
{
l1 = 3e-5;
}
testDarknetLayer("connected", true, true, l1);
}
TEST_P(Test_Darknet_layers, relu)