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Added complex meta tests for C++ and python (#14265)

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* Added complex meta tests for C++ and python

* Fixed python test

* Fixed code style

* Fixed python code style

* Added any test
This commit is contained in:
Ilya Churaev 2022-12-09 19:45:12 +04:00 committed by GitHub
parent 330e42a4ad
commit 7a7675a778
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
7 changed files with 257 additions and 6 deletions
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14
src/bindings/python/src/pyopenvino/graph/model.cpp
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@ -752,11 +752,17 @@ void regclass_graph_Model(py::module m) {
model.def(
"has_rt_info",
[](const ov::Model& self, const py::list& path) -> bool {
std::vector<std::string> cpp_args(path.size());
for (size_t i = 0; i < path.size(); i++) {
cpp_args[i] = path[i].cast<std::string>();
// FIXME: understand why has_rt_info causes Python crash
try {
std::vector<std::string> cpp_args(path.size());
for (size_t i = 0; i < path.size(); i++) {
cpp_args[i] = path[i].cast<std::string>();
}
self.get_rt_info<ov::Any>(cpp_args);
return true;
} catch (ov::Exception&) {
return false;
}
return self.has_rt_info(cpp_args);
},
py::arg("path"),
R"(

4
src/bindings/python/src/pyopenvino/utils/utils.hpp
View File

@ -56,6 +56,10 @@ inline ov::Any py_object_to_any(const py::object& py_obj) {
}
}
// In case of empty vector works like with vector of strings
if (_list.empty())
return _list.cast<std::vector<std::string>>();
switch (detected_type) {
case PY_TYPE::STR:
return _list.cast<std::vector<std::string>>();

80
src/bindings/python/tests/test_runtime/test_function.py
View File

@ -515,3 +515,83 @@ def test_serialize_rt_info(request):
os.remove(xml_path)
os.remove(bin_path)
# request - https://docs.pytest.org/en/7.1.x/reference/reference.html#request
def test_serialize_complex_rt_info(request):
def check_rt_info(model, serialized):
if serialized:
threshold = "13.23"
min_val = "-3.24543"
max_val = "3.23422"
directed = "YES"
empty = ""
ids = "sasd fdfdfsdf"
mean = "22.3 33.11 44"
else:
threshold = 13.23
min_val = -3.24543
max_val = 3.234223
directed = True
empty = []
ids = ["sasd", "fdfdfsdf"]
mean = [22.3, 33.11, 44.0]
assert model.has_rt_info(["config", "type_of_model"]) is True
assert model.has_rt_info(["config", "converter_type"]) is True
assert model.has_rt_info(["config", "model_parameters", "threshold"]) is True
assert model.has_rt_info(["config", "model_parameters", "min"]) is True
assert model.has_rt_info(["config", "model_parameters", "max"]) is True
assert model.has_rt_info(["config", "model_parameters", "labels", "label_tree", "type"]) is True
assert model.has_rt_info(["config", "model_parameters", "labels", "label_tree", "directed"]) is True
assert model.has_rt_info(["config", "model_parameters", "labels", "label_tree", "float_empty"]) is True
assert model.has_rt_info(["config", "model_parameters", "labels", "label_tree", "nodes"]) is True
assert model.has_rt_info(["config", "model_parameters", "labels", "label_groups", "ids"]) is True
assert model.has_rt_info(["config", "model_parameters", "mean_values"]) is True
assert model.get_rt_info(["config", "type_of_model"]) == "classification"
assert model.get_rt_info(["config", "converter_type"]) == "classification"
assert model.get_rt_info(["config", "model_parameters", "threshold"]) == threshold
assert model.get_rt_info(["config", "model_parameters", "min"]) == min_val
assert model.get_rt_info(["config", "model_parameters", "max"]) == max_val
assert model.get_rt_info(["config", "model_parameters", "labels", "label_tree", "type"]) == "tree"
assert model.get_rt_info(["config", "model_parameters", "labels", "label_tree", "directed"]) == directed
assert model.get_rt_info(["config", "model_parameters", "labels", "label_tree", "float_empty"]) == empty
assert model.get_rt_info(["config", "model_parameters", "labels", "label_tree", "nodes"]) == empty
assert model.get_rt_info(["config", "model_parameters", "labels", "label_groups", "ids"]) == ids
assert model.get_rt_info(["config", "model_parameters", "mean_values"]) == mean
core = Core()
xml_path, bin_path = create_filename_for_test(request.node.name)
input_shape = PartialShape([1])
param = ops.parameter(input_shape, dtype=np.float32, name="data")
relu1 = ops.relu(param, name="relu1")
relu1.get_output_tensor(0).set_names({"relu_t1"})
assert "relu_t1" in relu1.get_output_tensor(0).names
relu2 = ops.relu(relu1, name="relu2")
model = Model(relu2, [param], "TestFunction")
assert model is not None
model.set_rt_info("classification", ["config", "type_of_model"])
model.set_rt_info("classification", ["config", "converter_type"])
model.set_rt_info(13.23, ["config", "model_parameters", "threshold"])
model.set_rt_info(-3.24543, ["config", "model_parameters", "min"])
model.set_rt_info(3.234223, ["config", "model_parameters", "max"])
model.set_rt_info("tree", ["config", "model_parameters", "labels", "label_tree", "type"])
model.set_rt_info(True, ["config", "model_parameters", "labels", "label_tree", "directed"])
model.set_rt_info([], ["config", "model_parameters", "labels", "label_tree", "float_empty"])
model.set_rt_info([], ["config", "model_parameters", "labels", "label_tree", "nodes"])
model.set_rt_info(["sasd", "fdfdfsdf"], ["config", "model_parameters", "labels", "label_groups", "ids"])
model.set_rt_info([22.3, 33.11, 44.0], ["config", "model_parameters", "mean_values"])
check_rt_info(model, False)
serialize(model, xml_path, bin_path)
res_model = core.read_model(model=xml_path, weights=bin_path)
check_rt_info(res_model, True)
os.remove(xml_path)
os.remove(bin_path)
serialize(res_model, xml_path, bin_path)

3
src/core/src/any.cpp
View File

@ -63,7 +63,8 @@ void Any::Base::read_to(Base& other) const {
if (other.is<std::string>()) {
*static_cast<std::string*>(other.addressof()) = strm.str();
} else {
other.read(strm);
if (!strm.str().empty())
other.read(strm);
}
}

12
src/core/tests/any.cpp
View File

@ -539,4 +539,14 @@ TEST_F(AnyTests, AddressofNoThrow) {
ASSERT_EQ(nullptr, p.addressof());
p = 42;
ASSERT_NE(nullptr, p.addressof());
}
}
TEST_F(AnyTests, EmptyStringAsAny) {
Any p = "";
std::vector<float> ref_f;
std::vector<int> ref_i;
ASSERT_TRUE(p.is<std::string>());
ASSERT_EQ(p.as<int>(), 0);
ASSERT_EQ(p.as<std::vector<float>>(), ref_f);
ASSERT_EQ(p.as<std::vector<int>>(), ref_i);
}

68
src/core/tests/model.cpp
View File

@ -6,6 +6,7 @@
#include <gtest/gtest.h>
#include <memory>
#include <shared_node_info.hpp>
#include <test_common.hpp>
@ -1972,3 +1973,70 @@ TEST(model, set_meta_information) {
EXPECT_EQ(f->get_rt_info<std::string>({key, "test1"}), "1");
EXPECT_EQ(f->get_rt_info<int>({key, "test1"}), 1);
}
TEST(model, set_complex_meta_information) {
auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
arg0->set_friendly_name("data");
arg0->get_output_tensor(0).set_names({"input"});
auto relu = std::make_shared<ov::opset8::Relu>(arg0);
relu->set_friendly_name("relu");
relu->get_output_tensor(0).set_names({"relu_t", "identity"});
auto f = std::make_shared<ov::Model>(relu, ov::ParameterVector{arg0});
const auto check_rt_info = [](const std::shared_ptr<ov::Model>& model) {
EXPECT_TRUE(model->has_rt_info("config", "type_of_model"));
EXPECT_TRUE(model->has_rt_info("config", "converter_type"));
EXPECT_TRUE(model->has_rt_info("config", "model_parameters", "threshold"));
EXPECT_TRUE(model->has_rt_info("config", "model_parameters", "min"));
EXPECT_TRUE(model->has_rt_info("config", "model_parameters", "max"));
EXPECT_TRUE(model->has_rt_info("config", "model_parameters", "labels", "label_tree", "type"));
EXPECT_TRUE(model->has_rt_info("config", "model_parameters", "labels", "label_tree", "directed"));
EXPECT_TRUE(model->has_rt_info("config", "model_parameters", "labels", "label_tree", "nodes"));
EXPECT_TRUE(model->has_rt_info("config", "model_parameters", "labels", "label_groups", "ids"));
EXPECT_TRUE(model->has_rt_info("config", "model_parameters", "mean_values"));
EXPECT_EQ("classification", model->get_rt_info<std::string>("config", "type_of_model"));
EXPECT_EQ("classification", model->get_rt_info<std::string>("config", "converter_type"));
EXPECT_FLOAT_EQ(13.23f, model->get_rt_info<float>("config", "model_parameters", "threshold"));
EXPECT_FLOAT_EQ(-3.245433f, model->get_rt_info<float>("config", "model_parameters", "min"));
EXPECT_FLOAT_EQ(3.2342233f, model->get_rt_info<float>("config", "model_parameters", "max"));
EXPECT_EQ("tree",
model->get_rt_info<std::string>("config", "model_parameters", "labels", "label_tree", "type"));
EXPECT_EQ(true, model->get_rt_info<bool>("config", "model_parameters", "labels", "label_tree", "directed"));
EXPECT_EQ(std::vector<std::string>{},
model->get_rt_info<std::vector<std::string>>("config",
"model_parameters",
"labels",
"label_tree",
"nodes"));
std::vector<std::string> str_vec{"sasd", "fdfdfsdf"};
EXPECT_EQ(str_vec,
model->get_rt_info<std::vector<std::string>>("config",
"model_parameters",
"labels",
"label_groups",
"ids"));
std::vector<float> fl_vec{22.3f, 33.11f, 44.f};
EXPECT_EQ(fl_vec, model->get_rt_info<std::vector<float>>("config", "model_parameters", "mean_values"));
};
// Fill meta data
f->set_rt_info("classification", "config", "type_of_model");
f->set_rt_info("classification", "config", "converter_type");
f->set_rt_info(13.23f, "config", "model_parameters", "threshold");
f->set_rt_info(-3.245433f, "config", "model_parameters", "min");
f->set_rt_info(3.2342233f, "config", "model_parameters", "max");
f->set_rt_info("tree", "config", "model_parameters", "labels", "label_tree", "type");
f->set_rt_info(true, "config", "model_parameters", "labels", "label_tree", "directed");
f->set_rt_info(std::vector<std::string>{}, "config", "model_parameters", "labels", "label_tree", "nodes");
f->set_rt_info(std::vector<std::string>{"sasd", "fdfdfsdf"},
"config",
"model_parameters",
"labels",
"label_groups",
"ids");
f->set_rt_info(std::vector<float>{22.3f, 33.11f, 44.f}, "config", "model_parameters", "mean_values");
check_rt_info(f);
}

82
src/core/tests/pass/serialization/serialize.cpp
View File

@ -406,3 +406,85 @@ TEST_F(MetaDataSerialize, get_meta_serialized_changed_meta) {
check_meta_info(s_model);
}
}
TEST_F(MetaDataSerialize, set_complex_meta_information) {
const auto check_rt_info = [](const std::shared_ptr<ov::Model>& model) {
EXPECT_TRUE(model->has_rt_info("config", "type_of_model"));
EXPECT_TRUE(model->has_rt_info("config", "converter_type"));
EXPECT_TRUE(model->has_rt_info("config", "model_parameters", "threshold"));
EXPECT_TRUE(model->has_rt_info("config", "model_parameters", "min"));
EXPECT_TRUE(model->has_rt_info("config", "model_parameters", "max"));
EXPECT_TRUE(model->has_rt_info("config", "model_parameters", "labels", "label_tree", "type"));
EXPECT_TRUE(model->has_rt_info("config", "model_parameters", "labels", "label_tree", "directed"));
EXPECT_TRUE(model->has_rt_info("config", "model_parameters", "labels", "label_tree", "nodes"));
EXPECT_TRUE(model->has_rt_info("config", "model_parameters", "labels", "label_tree", "float_empty"));
EXPECT_TRUE(model->has_rt_info("config", "model_parameters", "labels", "label_groups", "ids"));
EXPECT_TRUE(model->has_rt_info("config", "model_parameters", "mean_values"));
EXPECT_EQ("classification", model->get_rt_info<std::string>("config", "type_of_model"));
EXPECT_EQ("classification", model->get_rt_info<std::string>("config", "converter_type"));
EXPECT_GE(0.0001f, model->get_rt_info<float>("config", "model_parameters", "threshold") - 13.23f);
EXPECT_GE(0.0001f, model->get_rt_info<float>("config", "model_parameters", "min") - (-3.245433f));
EXPECT_GE(0.0001f, model->get_rt_info<float>("config", "model_parameters", "max") - 3.2342233f);
EXPECT_EQ("tree",
model->get_rt_info<std::string>("config", "model_parameters", "labels", "label_tree", "type"));
EXPECT_EQ(true, model->get_rt_info<bool>("config", "model_parameters", "labels", "label_tree", "directed"));
EXPECT_EQ(std::vector<std::string>{},
model->get_rt_info<std::vector<std::string>>("config",
"model_parameters",
"labels",
"label_tree",
"nodes"));
EXPECT_EQ(std::vector<float>{},
model->get_rt_info<std::vector<float>>("config",
"model_parameters",
"labels",
"label_tree",
"float_empty"));
std::vector<std::string> str_vec{"sasd", "fdfdfsdf"};
EXPECT_EQ(str_vec,
model->get_rt_info<std::vector<std::string>>("config",
"model_parameters",
"labels",
"label_groups",
"ids"));
std::vector<float> fl_vec{22.3f, 33.11f, 44.f};
EXPECT_EQ(fl_vec, model->get_rt_info<std::vector<float>>("config", "model_parameters", "mean_values"));
};
auto model = ov::test::readModel(ir_with_meta);
{
auto& rt_info = model->get_rt_info();
ASSERT_FALSE(rt_info.empty());
check_meta_info(model);
// Fill meta data
model->set_rt_info("classification", "config", "type_of_model");
model->set_rt_info("classification", "config", "converter_type");
model->set_rt_info(13.23f, "config", "model_parameters", "threshold");
model->set_rt_info(-3.245433f, "config", "model_parameters", "min");
model->set_rt_info(3.2342233f, "config", "model_parameters", "max");
model->set_rt_info("tree", "config", "model_parameters", "labels", "label_tree", "type");
model->set_rt_info(true, "config", "model_parameters", "labels", "label_tree", "directed");
model->set_rt_info(std::vector<float>{}, "config", "model_parameters", "labels", "label_tree", "float_empty");
model->set_rt_info(std::vector<std::string>{}, "config", "model_parameters", "labels", "label_tree", "nodes");
model->set_rt_info(std::vector<std::string>{"sasd", "fdfdfsdf"},
"config",
"model_parameters",
"labels",
"label_groups",
"ids");
model->set_rt_info(std::vector<float>{22.3f, 33.11f, 44.f}, "config", "model_parameters", "mean_values");
check_rt_info(model);
}
// Serialize the model
ov::serialize(model, m_out_xml_path, m_out_bin_path);
auto s_model = ov::test::readModel(m_out_xml_path, m_out_bin_path);
{
check_meta_info(s_model);
check_rt_info(s_model);
}
}