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Merge branch 4.x

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This commit is contained in:
Alexander Smorkalov
2024-07-01 15:15:08 +03:00
parent 12b8ed1443 39a7b3d186
commit 3abd9f2a28
62 changed files with 2178 additions and 243 deletions
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modules/dnn/test/test_int8_layers.cpp
+55
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@@ -517,6 +517,61 @@ TEST_P(Test_Int8_layers, Eltwise)
testLayer("split_max", "ONNX", 0.004, 0.012);
}
TEST_P(Test_Int8_layers, DepthSpaceOps) {
auto test_layer_with_onnx_conformance_models = [&](const std::string &model_name, double l1, double lInf) {
std::string model_path = _tf("onnx/conformance/node/test_" + model_name + "/model.onnx");
auto net = readNet(model_path);
// load reference inputs and outputs
std::string data_base_path = _tf("onnx/conformance/node/test_" + model_name + "/test_data_set_0");
Mat input = readTensorFromONNX(data_base_path + "/input_0.pb");
Mat ref_output = readTensorFromONNX(data_base_path + "/output_0.pb");
std::vector<float> input_scales, output_scales;
std::vector<int> input_zeropoints, output_zeropoints;
auto qnet = net.quantize(std::vector<Mat>{input}, CV_8S, CV_8S, false);
qnet.getInputDetails(input_scales, input_zeropoints);
qnet.getOutputDetails(output_scales, output_zeropoints);
qnet.setPreferableBackend(backend);
qnet.setPreferableTarget(target);
Mat quantized_input, quantized_output;
input.convertTo(quantized_input, CV_8S, 1.f / input_scales.front(), input_zeropoints.front());
qnet.setInput(quantized_input);
quantized_output = qnet.forward();
Mat output;
quantized_output.convertTo(output, CV_32F, output_scales.front(), -(output_scales.front() * output_zeropoints.front()));
normAssert(ref_output, output, model_name.c_str(), l1, lInf);
};
double l1 = default_l1, lInf = default_lInf;
{
l1 = 0.001; lInf = 0.002;
if (backend == DNN_BACKEND_TIMVX) { l1 = 0.001; lInf = 0.002; }
test_layer_with_onnx_conformance_models("spacetodepth", l1, lInf);
}
{
l1 = 0.022; lInf = 0.044;
if (backend == DNN_BACKEND_TIMVX) { l1 = 0.022; lInf = 0.044; }
test_layer_with_onnx_conformance_models("spacetodepth_example", l1, lInf);
}
{
l1 = 0.001; lInf = 0.002;
if (backend == DNN_BACKEND_TIMVX) { l1 = 0.24; lInf = 0.99; }
test_layer_with_onnx_conformance_models("depthtospace_crd_mode", l1, lInf);
}
test_layer_with_onnx_conformance_models("depthtospace_dcr_mode", 0.001, 0.002);
test_layer_with_onnx_conformance_models("depthtospace_example", 0.07, 0.14);
{
l1 = 0.07; lInf = 0.14;
if (backend == DNN_BACKEND_TIMVX) // diff too huge, l1 = 13.6; lInf = 27.2
applyTestTag(CV_TEST_TAG_DNN_SKIP_TIMVX);
test_layer_with_onnx_conformance_models("depthtospace_crd_mode_example", l1, lInf);
}
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Int8_layers, dnnBackendsAndTargetsInt8());
class Test_Int8_nets : public DNNTestLayer
modules/dnn/test/test_onnx_conformance_layer_filter__openvino.inl.hpp
+10
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@@ -480,10 +480,20 @@ CASE(test_cumsum_2d_negative_axis)
// no filter
CASE(test_depthtospace_crd_mode)
// no filter
if (target == DNN_TARGET_OPENCL)
{
default_l1 = 1e-4; // Expected: (normL1) <= (l1), actual: 9.33057e-05 vs 1e-05
default_lInf = 2.5e-4; // Expected: (normInf) <= (lInf), actual: 0.000243843 vs 0.0001
}
CASE(test_depthtospace_crd_mode_example)
// no filter
CASE(test_depthtospace_dcr_mode)
// no filter
if (target == DNN_TARGET_OPENCL)
{
default_l1 = 1e-4; // Expected: (normL1) <= (l1), actual: 9.33057e-05 vs 1e-05
default_lInf = 2.5e-4; // Expected: (normInf) <= (lInf), actual: 0.000243843 vs 0.0001
}
CASE(test_depthtospace_example)
// no filter
CASE(test_dequantizelinear)