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Commit 57d637bc authored by Michał Karzyński's avatar Michał Karzyński Committed by Scott Cyphers
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[ONNX] Split ONNX tests into smaller files in test/onnx subdirectory (#2779) 

* Move ONNX tests to the test/onnx subdirectory

* Change non-conforming test names

* Refactor quant and reshape tests to separate files

* Refactor tests into separate files

* Rename test in GPU excludes
parent a588ca28
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Showing with 1817 additions and 1650 deletions
src/ngraph/runtime/gpu/unit_test.manifest
View file @ 57d637bc
......@@ -123,7 +123,7 @@ model_dequantize_linear_1d_zero_scale_int8_4d
model_dequantize_linear_1d_zero_scale_uint8_negative_axis
model_quantize_linear
model_quantize_linear_zero_point
quantize_linear_axis_zero
model_quantize_linear_axis_zero
model_quantize_linear_axis_negative
model_quant_conv_linear
model_quant_conv_linear_2d
......
test/CMakeLists.txt
View file @ 57d637bc
......@@ -165,9 +165,14 @@ if (NGRAPH_CPU_ENABLE)
endif()
if (NGRAPH_ONNX_IMPORT_ENABLE)
list(APPEND MULTI_TEST_SRC onnx_import.in.cpp)
list(APPEND MULTI_TEST_SRC
onnx/onnx_import.in.cpp
onnx/onnx_import_convpool.in.cpp
onnx/onnx_import_reshape.in.cpp
onnx/onnx_import_rnn.in.cpp
onnx/onnx_import_quant.in.cpp)
if (NGRAPH_ONNXIFI_ENABLE)
list(APPEND SRC onnxifi.cpp onnxifi_span.cpp)
list(APPEND SRC onnx/onnxifi.cpp onnx/onnxifi_span.cpp)
endif()
endif()
......
test/onnx_import.in.cpp → test/onnx/onnx_import.in.cpp
View file @ 57d637bc
......@@ -40,6 +40,7 @@ static std::string s_manifest = "${MANIFEST}";
using Inputs = std::vector<std::vector<float>>;
using Outputs = std::vector<std::vector<float>>;
// ############################################################################ CORE TESTS
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_output_names_check)
{
auto function = onnx_import::import_onnx_model(
......@@ -89,232 +90,249 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_add_abc_initializers)
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_addmul_abc)
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_override_op)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/addmul_abc.prototxt"));
onnx_import::register_operator(
"FalseAdd", 1, "", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
return {std::make_shared<ngraph::op::Add>(ng_inputs.at(0), ng_inputs.at(1))};
});
std::vector<std::vector<float>> inputs;
onnx_import::register_operator(
"FalseAdd", 1, "", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
return {std::make_shared<ngraph::op::Subtract>(ng_inputs.at(0), ng_inputs.at(1))};
});
Shape shape{1, 2, 2};
inputs.emplace_back(test::NDArray<float, 3>({{{9, 10}}, {{11, 12}}}).get_vector());
inputs.emplace_back(test::NDArray<float, 3>({{{5, 6}}, {{7, 8}}}).get_vector());
inputs.emplace_back(test::NDArray<float, 3>({{{1, 2}}, {{3, 4}}}).get_vector());
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/override_op.prototxt"));
auto expected_output = test::NDArray<float, 3>({{{46, 62}}, {{80, 100}}}).get_vector();
Inputs inputs;
inputs.emplace_back(std::vector<float>{0.f, 1.f, 2.f, 3.f});
inputs.emplace_back(std::vector<float>{3.f, 2.f, 1.f, 0.f});
auto result_vectors = execute(function, inputs, "${BACKEND_NAME}");
EXPECT_TRUE(test::all_close_f(expected_output, result_vectors.front()));
Outputs expected_output{std::vector<float>{-3.f, -1.f, 1.f, 3.f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_argmin_no_keepdims)
NGRAPH_TEST(onnx_${BACKEND_NAME}, import_non_existing_file)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/argmin_no_keepdims.prototxt"));
Inputs inputs{test::NDArray<float, 2>{{2, 1}, {3, 10}}.get_vector()};
std::vector<std::vector<int64_t>> expected_output{{1, 0}};
std::vector<std::vector<int64_t>> result{
execute<float, int64_t>(function, inputs, "${BACKEND_NAME}")};
EXPECT_EQ(expected_output, result);
try
{
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/i.dont.exist"));
}
catch (const std::runtime_error& exc)
{
// asserts that an exception was thrown and that the error message contains the file name
std::string msg{exc.what()};
EXPECT_TRUE(msg.find("i.dont.exist") != std::string::npos);
}
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_split_equal_parts_default)
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_unsupported_op)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/split_equal_parts_default.prototxt"));
Inputs inputs{{1, 2, 3, 4, 5, 6}};
Outputs expected_outputs{{1, 2}, {3, 4}, {5, 6}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_EQ(outputs.size(), expected_outputs.size());
for (std::size_t i = 0; i < expected_outputs.size(); ++i)
try
{
onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/unsupported_op.prototxt"));
FAIL() << "Expected ngraph::ngraph_error";
}
catch (ngraph::ngraph_error const& err)
{
std::string what{err.what()};
EXPECT_NE(what.find("nGraph does not support"), std::string::npos);
EXPECT_NE(what.find("FakeOpName"), std::string::npos);
EXPECT_NE(what.find("AnotherFakeOpName"), std::string::npos);
}
catch (...)
{
EXPECT_EQ(outputs[i].size(), expected_outputs[i].size());
EXPECT_TRUE(test::all_close_f(outputs[i], expected_outputs[i]));
FAIL() << "Expected ngraph::ngraph_error";
}
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_split_equal_parts_2d)
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_custom_op)
{
// Split into 2 equal parts along axis=1
onnx_import::register_operator(
"AddQ", 1, "com.intel.ai", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
return {std::make_shared<ngraph::op::Add>(ng_inputs.at(0), ng_inputs.at(1))};
});
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/split_equal_parts_2d.prototxt"));
file_util::path_join(SERIALIZED_ZOO, "onnx/custom_operator.prototxt"));
Inputs inputs{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}};
Outputs expected_outputs{{0, 1, 2, 6, 7, 8}, {3, 4, 5, 9, 10, 11}};
Inputs inputs{{1, 2, 3, 4}};
Outputs expected_outputs{{3, 6, 9, 12}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_EQ(outputs.size(), expected_outputs.size());
for (std::size_t i = 0; i < expected_outputs.size(); ++i)
{
EXPECT_EQ(outputs[i].size(), expected_outputs[i].size());
EXPECT_TRUE(test::all_close_f(outputs[i], expected_outputs[i]));
}
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_split_variable_parts_2d)
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_custom_op_default_domain)
{
// Split into variable parts {2, 4} along axis=1
onnx_import::register_operator(
"AddQ", 1, "com.intel.ai", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
return {std::make_shared<ngraph::op::Add>(ng_inputs.at(0), ng_inputs.at(1))};
});
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/split_variable_parts_2d.prototxt"));
file_util::path_join(SERIALIZED_ZOO, "onnx/custom_operator_default_domain.prototxt"));
Inputs inputs{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}};
Outputs expected_outputs{{0, 1, 6, 7}, {2, 3, 4, 5, 8, 9, 10, 11}};
Inputs inputs{{1, 2, 3, 4}};
Outputs expected_outputs{{3, 6, 9, 12}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_EQ(outputs.size(), expected_outputs.size());
for (std::size_t i = 0; i < expected_outputs.size(); ++i)
{
EXPECT_EQ(outputs[i].size(), expected_outputs[i].size());
EXPECT_TRUE(test::all_close_f(outputs[i], expected_outputs[i]));
}
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
namespace
NGRAPH_TEST(onnx_${BACKEND_NAME}, is_op_supported)
{
std::vector<std::vector<float>> conv2d_execute(const std::shared_ptr<Function>& function)
{
std::vector<std::vector<float>> args;
// data (1, 1, 7, 5) input tensor
args.emplace_back(test::NDArray<float, 4>{{{{{0.f, 1.f, 2.f, 3.f, 4.f},
{5.f, 6.f, 7.f, 8.f, 9.f},
{10.f, 11.f, 12.f, 13.f, 14.f},
{15.f, 16.f, 17.f, 18.f, 19.f},
{20.f, 21.f, 22.f, 23.f, 24.f},
{25.f, 26.f, 27.f, 28.f, 29.f},
{30.f, 31.f, 32.f, 33.f, 34.f}}}}}
.get_vector());
// filters (1, 1, 3, 3) aka convolution weights
args.emplace_back(
test::NDArray<float, 4>{{{{{1.f, 1.f, 1.f}, {1.f, 1.f, 1.f}, {1.f, 1.f, 1.f}}}}}
.get_vector());
return execute(function, args, "${BACKEND_NAME}");
}
// Simple case
EXPECT_TRUE(onnx_import::is_operator_supported("Sum", 1, "ai.onnx"));
// With fallback
EXPECT_TRUE(onnx_import::is_operator_supported("Sum", 100, "ai.onnx"));
} // namespace
// Different opset versions
EXPECT_TRUE(onnx_import::is_operator_supported("Add", 1, "ai.onnx"));
EXPECT_TRUE(onnx_import::is_operator_supported("Add", 7, "ai.onnx"));
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_conv2d_strides_padding)
{
// Convolution with strides=2 and padding=1
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/conv_with_strides_padding.prototxt"));
// Default domain name
EXPECT_TRUE(onnx_import::is_operator_supported("Sum", 1));
// Unregistered operator
EXPECT_FALSE(onnx_import::is_operator_supported("DummyOp", 1));
EXPECT_FALSE(onnx_import::is_operator_supported("DummyOp", 1, "ai.onnx"));
EXPECT_FALSE(onnx_import::is_operator_supported("DummyOp", 10, "ai.onnx"));
// (1, 1, 4, 3)
auto expected_output = test::NDArray<float, 4>({{{{12.f, 27.f, 24.f},
{63.f, 108.f, 81.f},
{123.f, 198.f, 141.f},
{112.f, 177.f, 124.f}}}})
.get_vector();
// Operator with bad domain name
EXPECT_FALSE(onnx_import::is_operator_supported("Sum", 1, "bad.domain"));
auto result = conv2d_execute(function);
EXPECT_EQ(expected_output, result.front());
// Registered custom operator
onnx_import::register_operator(
"AddQ", 1, "com.intel.ai", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
return {std::make_shared<ngraph::op::Add>(ng_inputs.at(0), ng_inputs.at(1))};
});
EXPECT_TRUE(onnx_import::is_operator_supported("AddQ", 1, "com.intel.ai"));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_conv2d_strides_no_padding)
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_missing_op_domain)
{
// Convolution with strides=2 and padding=1
onnx_import::register_operator(
"CustomAdd", 1, "custom.op", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
return {std::make_shared<ngraph::op::Add>(ng_inputs.at(0), ng_inputs.at(1))};
});
EXPECT_TRUE(onnx_import::is_operator_supported("CustomAdd", 1, "custom.op"));
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/conv_with_strides_no_padding.prototxt"));
file_util::path_join(SERIALIZED_ZOO, "onnx/missing_op_domain.prototxt"));
// (1, 1, 3, 2)
auto expected_output =
test::NDArray<float, 4>({{{{54.f, 72.f}, {144.f, 162.f}, {234.f, 252.f}}}}).get_vector();
Inputs inputs;
inputs.emplace_back(std::vector<float>{0.f, 1.f, 2.f, 3.f});
inputs.emplace_back(std::vector<float>{0.f, 1.f, 2.f, 3.f});
Outputs expected_output{std::vector<float>{0.f, 2.f, 4.f, 6.f}};
auto result = conv2d_execute(function);
EXPECT_EQ(expected_output, result.front());
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_conv2d_strides_assymetric_padding)
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_missing_input)
{
// Convolution with strides=2 and padding=1
auto function = onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/conv_with_strides_and_asymmetric_padding.prototxt"));
onnx_import::register_operator(
"TestMissingInOut", 1, "com.intel.ai", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
std::shared_ptr<ngraph::Node> A = ng_inputs.at(0);
std::shared_ptr<ngraph::Node> B = ng_inputs.at(1);
std::shared_ptr<ngraph::Node> C = ng_inputs.at(2);
// (1, 1, 4, 2)
auto expected_output =
test::NDArray<float, 4>({{{{21.f, 33.f}, {99.f, 117.f}, {189.f, 207.f}, {171.f, 183.f}}}})
.get_vector();
A = A * C;
if (!B->is_null())
{
B = B / C;
}
auto result = conv2d_execute(function);
EXPECT_EQ(expected_output, result.front());
}
C = C + C;
return {A, B, C};
});
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_average_pool_2d)
{
// Pooling with strides=2 and no padding
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/average_pool_2d.prototxt"));
onnx_import::register_operator(
"TestMissingIn", 1, "com.intel.ai", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
std::shared_ptr<ngraph::Node> result = std::make_shared<ngraph::op::Constant>(
element::f32, ngraph::Shape{2, 2}, std::vector<float>{1, 1, 1, 1});
// input data shape (1, 1, 4, 4)
Inputs inputs;
inputs.push_back(test::NDArray<float, 4>({{{{0.f, 1.f, 2.f, 3.f},
{4.f, 5.f, 6.f, 7.f},
{8.f, 9.f, 10.f, 11.f},
{12.f, 13.f, 14.f, 15.f}}}})
.get_vector());
for (const auto& ng_input : ng_inputs)
{
if (!ng_input->is_null())
{
result = ng_input * result;
}
}
return {result};
});
// (1, 1, 2, 2)
auto expected_output = test::NDArray<float, 4>({{{{2.5f, 4.5f}, {10.5f, 12.5f}}}}).get_vector();
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/missing_input.prototxt"));
Inputs inputs{{1, 2, 3, 4}, {5, 6, 7, 8}};
Outputs expected_outputs{{50, 144, 294, 512}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_EQ(expected_output, outputs.front());
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_average_pool_2d_pads)
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_initializer_wo_input)
{
// Pooling with strides=2 and padding=1
// This test checks a model which has an initializer, but no input with the same name
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/average_pool_2d_pads.prototxt"));
file_util::path_join(SERIALIZED_ZOO, "onnx/initializer_wo_input.prototxt"));
// input data shape (1, 1, 4, 4)
Inputs inputs;
inputs.push_back(test::NDArray<float, 4>({{{{0.f, 1.f, 2.f, 3.f},
{4.f, 5.f, 6.f, 7.f},
{8.f, 9.f, 10.f, 11.f},
{12.f, 13.f, 14.f, 15.f}}}})
.get_vector());
// (1, 1, 3, 3)
auto expected_output =
test::NDArray<float, 4>({{{{0.f, 1.5f, 3.f}, {6.f, 7.5f, 9.f}, {12.f, 13.5f, 15.f}}}})
.get_vector();
inputs.emplace_back(std::vector<float>{0, 1, 2, 3, 4, 5});
Outputs outputs = execute(function, inputs, "${BACKEND_NAME}");
std::vector<float> expected_output{0, 2, 6, 12, 20, 30};
EXPECT_EQ(expected_output, outputs.front());
Outputs output{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output, output.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_max_pool_2d_pads)
// ############################################################################ OPERATOR TESTS
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_addmul_abc)
{
// Pooling with strides=2 and padding=1
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/max_pool_2d_pads.prototxt"));
file_util::path_join(SERIALIZED_ZOO, "onnx/addmul_abc.prototxt"));
// input data shape (1, 1, 4, 4)
Inputs inputs;
inputs.push_back(test::NDArray<float, 4>({{{{0.f, 1.f, 2.f, 3.f},
{4.f, 5.f, 6.f, 7.f},
{8.f, 9.f, 10.f, 11.f},
{12.f, 13.f, 14.f, 15.f}}}})
.get_vector());
std::vector<std::vector<float>> inputs;
// (1, 1, 3, 3)
auto expected_output =
test::NDArray<float, 4>({{{{0.f, 2.f, 3.f}, {8.f, 10.f, 11.f}, {12.f, 14.f, 15.f}}}})
.get_vector();
Shape shape{1, 2, 2};
inputs.emplace_back(test::NDArray<float, 3>({{{9, 10}}, {{11, 12}}}).get_vector());
inputs.emplace_back(test::NDArray<float, 3>({{{5, 6}}, {{7, 8}}}).get_vector());
inputs.emplace_back(test::NDArray<float, 3>({{{1, 2}}, {{3, 4}}}).get_vector());
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
auto expected_output = test::NDArray<float, 3>({{{46, 62}}, {{80, 100}}}).get_vector();
auto result_vectors = execute(function, inputs, "${BACKEND_NAME}");
EXPECT_TRUE(test::all_close_f(expected_output, result_vectors.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_argmin_no_keepdims)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/argmin_no_keepdims.prototxt"));
EXPECT_EQ(expected_output, outputs.front());
Inputs inputs{test::NDArray<float, 2>{{2, 1}, {3, 10}}.get_vector()};
std::vector<std::vector<int64_t>> expected_output{{1, 0}};
std::vector<std::vector<int64_t>> result{
execute<float, int64_t>(function, inputs, "${BACKEND_NAME}")};
EXPECT_EQ(expected_output, result);
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_batchnorm_default)
......@@ -542,38 +560,6 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_softmax)
EXPECT_TRUE(test::all_close_f(expected_output, result_vectors.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_concat)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/concat.prototxt"));
Inputs inputs;
inputs.emplace_back(test::NDArray<float, 1>({1, 2}).get_vector());
inputs.emplace_back(test::NDArray<float, 1>({3, 4}).get_vector());
Outputs expected_outputs{test::NDArray<float, 1>({1, 2, 3, 4}).get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_flatten)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/flatten.prototxt"));
Inputs inputs;
inputs.emplace_back(
test::NDArray<float, 4>({{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}}).get_vector());
Outputs expected_outputs{test::NDArray<float, 3>({{{1, 2, 3, 4}, {5, 6, 7, 8}}}).get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_sub)
{
auto function =
......@@ -590,52 +576,10 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_sub)
EXPECT_TRUE(test::all_close_f(expected_output, result_vectors.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_unsqueeze)
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_div)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/unsqueeze.prototxt"));
Inputs inputs;
inputs.emplace_back(test::NDArray<float, 3>(
{{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}},
{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}},
{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}}})
.get_vector());
Outputs expected_output{
test::NDArray<float, 4>(
{{{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}},
{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}},
{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_squeeze)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/squeeze_duplicate_axes.prototxt"));
// {1, 4, 1, 1, 2}
Inputs inputs{test::NDArray<float, 5>(
{{{{{1.0f, 2.0f}}}, {{{3.0f, 4.0f}}}, {{{5.0f, 6.0f}}}, {{{7.0f, 8.0f}}}}})
.get_vector()};
// {4, 2}
Outputs expected_output{
test::NDArray<float, 2>({{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}, {7.0f, 8.0f}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_div)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/div.prototxt"));
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/div.prototxt"));
Inputs inputs;
inputs.emplace_back(test::NDArray<float, 3>({{{1, 2, 3}}}).get_vector());
......@@ -673,150 +617,6 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_add_bcast)
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_reshape_reduced_dims)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_reduced_dims.prototxt"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (2, 12)
Outputs expected_outputs{
test::NDArray<float, 2>({{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11},
{12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_reshape_reordered_dims)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_reordered_dims.prototxt"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (4, 2, 3)
Outputs expected_outputs{test::NDArray<float, 3>({{{0, 1, 2}, {3, 4, 5}},
{{6, 7, 8}, {9, 10, 11}},
{{12, 13, 14}, {15, 16, 17}},
{{18, 19, 20}, {21, 22, 23}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_reshape_extended_dims)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_extended_dims.prototxt"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (3, 2, 2, 2)
Outputs expected_outputs{test::NDArray<float, 4>({{{{0, 1}, {2, 3}}, {{4, 5}, {6, 7}}},
{{{8, 9}, {10, 11}}, {{12, 13}, {14, 15}}},
{{{16, 17}, {18, 19}}, {{20, 21}, {22, 23}}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_reshape_single_dim)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_single_dim.prototxt"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (24, )
Outputs expected_outputs{
test::NDArray<float, 1>(
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_reshape_negative_dim)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_negative_dim.prototxt"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (6, 2, 2)
Outputs expected_outputs{test::NDArray<float, 3>({{{0, 1}, {2, 3}},
{{4, 5}, {6, 7}},
{{8, 9}, {10, 11}},
{{12, 13}, {14, 15}},
{{16, 17}, {18, 19}},
{{20, 21}, {22, 23}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_reshape_negative_with_zero_dim)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_negative_with_zero_dims.prototxt"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (2, 6, 2)
Outputs expected_outputs{
test::NDArray<float, 3>({{{0, 1}, {2, 3}, {4, 5}, {6, 7}, {8, 9}, {10, 11}},
{{12, 13}, {14, 15}, {16, 17}, {18, 19}, {20, 21}, {22, 23}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_reshape_output_shape_as_input)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_output_shape_as_input.prototxt"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (2, 6, 2)
Outputs expected_outputs{
test::NDArray<float, 3>({{{0, 1}, {2, 3}, {4, 5}, {6, 7}, {8, 9}, {10, 11}},
{{12, 13}, {14, 15}, {16, 17}, {18, 19}, {20, 21}, {22, 23}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_reduce_log_sum)
{
auto function = onnx_import::import_onnx_model(
......@@ -1084,7 +884,7 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_leaky_relu)
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, prelu)
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_prelu)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/prelu.prototxt"));
......@@ -1329,217 +1129,6 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_thresholded_relu)
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_unsupported_op)
{
try
{
onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/unsupported_op.prototxt"));
FAIL() << "Expected ngraph::ngraph_error";
}
catch (ngraph::ngraph_error const& err)
{
std::string what{err.what()};
EXPECT_NE(what.find("nGraph does not support"), std::string::npos);
EXPECT_NE(what.find("FakeOpName"), std::string::npos);
EXPECT_NE(what.find("AnotherFakeOpName"), std::string::npos);
}
catch (...)
{
FAIL() << "Expected ngraph::ngraph_error";
}
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_custom_op)
{
onnx_import::register_operator(
"AddQ", 1, "com.intel.ai", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
return {std::make_shared<ngraph::op::Add>(ng_inputs.at(0), ng_inputs.at(1))};
});
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/custom_operator.prototxt"));
Inputs inputs{{1, 2, 3, 4}};
Outputs expected_outputs{{3, 6, 9, 12}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_custom_op_default_domain)
{
onnx_import::register_operator(
"AddQ", 1, "com.intel.ai", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
return {std::make_shared<ngraph::op::Add>(ng_inputs.at(0), ng_inputs.at(1))};
});
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/custom_operator_default_domain.prototxt"));
Inputs inputs{{1, 2, 3, 4}};
Outputs expected_outputs{{3, 6, 9, 12}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_conv2d_dilation_assymetric_pads_strides)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/conv2d_dilation_assym_pads_strides.prototxt"));
// "", // auto_pad
// vector<int64_t>{1, 1}, // dilations
// 1, // group
// vector<int64_t>{3, 3}, // kernel_shape
// vector<int64_t>{1, 1, 1, 2}, // pads
// vector<int64_t>{3, 1} // strides
Inputs inputs;
// {2, 1, 1, 1}
inputs.emplace_back(
test::NDArray<float, 4>({{{{-0.09103918075561523f}}}, {{{-0.32513630390167236f}}}})
.get_vector());
// {2, 1, 3, 3}
inputs.emplace_back(
test::NDArray<float, 4>(
{{{{0.4312484860420227f, -0.12559029459953308f, 0.44889551401138306f},
{-0.3100617825984955f, 0.13522827625274658f, -0.06791308522224426f},
{0.22671669721603394f, -0.17391827702522278f, -0.31299442052841187f}}},
{{{-0.31545522809028625f, 0.06560015678405762f, 0.2656586766242981f},
{0.41363757848739624f, 0.31231558322906494f, -0.376018226146698f},
{-0.005708813667297363f, 0.34922850131988525f, 0.45095211267471313f}}}})
.get_vector());
// {2, 2, 1, 2}
Outputs expected_output{
test::NDArray<float, 4>({{{{-0.012311071157455444f, 0.02822777070105076f}},
{{-0.028432954102754593f, -0.037657227367162704f}}},
{{{-0.04396762326359749f, 0.10081233829259872f}},
{{-0.10154513269662857f, -0.13448859751224518f}}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_conv3d_bias)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/conv3d_bias.prototxt"));
// "", // auto_pad
// vector<int64_t>{2, 2, 2}, // dilations
// 1, // group
// vector<int64_t>{2, 2, 2}, // kernel_shape
// vector<int64_t>{2, 2, 2, 2, 2, 2}, // pads
// vector<int64_t>{2, 2, 2} // strides
Inputs inputs;
// X: {2, 1, 4, 4, 4}
inputs.emplace_back(
std::vector<float>{0.46796226501464844f, -0.4613912105560303f, 0.33512794971466064f,
-0.4010460674762726f, 0.41722816228866577f, -0.048133403062820435f,
0.20415884256362915f, 0.03189706802368164f, -0.04779183864593506f,
-0.0795503556728363f, 0.4987630844116211f, 0.3506373167037964f,
0.48065757751464844f, 0.269855260848999f, -0.2463444471359253f,
0.19044137001037598f, -0.11830493807792664f, -0.2576887905597687f,
-0.33940935134887695f, -0.257951021194458f, -0.08279827237129211f,
0.3513314127922058f, -0.29122066497802734f, -0.43358397483825684f,
-0.13429927825927734f, 0.44032156467437744f, 0.05308258533477783f,
-0.3499870300292969f, -0.28474611043930054f, -0.44209951162338257f,
-0.07418054342269897f, -0.10919415950775146f, 0.2845439314842224f,
0.3498746156692505f, -0.19313520193099976f, 0.32609254121780396f,
0.4880145788192749f, 0.05574071407318115f, -0.46457427740097046f,
-0.02524462342262268f, -0.18780940771102905f, -0.14720159769058228f,
0.207585871219635f, 0.47157740592956543f, -0.05567386746406555f,
-0.49871665239334106f, 0.2274145483970642f, 0.4589425325393677f,
-0.4725189805030823f, -0.4358765780925751f, 0.2841453552246094f,
-0.27037882804870605f, 0.34227508306503296f, 0.33575427532196045f,
-0.19485199451446533f, -0.27679920196533203f, -0.4238079786300659f,
-0.4385119676589966f, 0.43724071979522705f, 0.3065117597579956f,
0.45696544647216797f, 0.05291992425918579f, -0.023618370294570923f,
-0.1860884726047516f, 0.08669537305831909f, 0.32541000843048096f,
0.1846179962158203f, -0.1984834372997284f, -0.2754465937614441f,
0.32004624605178833f, -0.34846532344818115f, 0.0999596118927002f,
-0.11374691128730774f, 0.21225297451019287f, -0.02315312623977661f,
0.1671370267868042f, 0.22319108247756958f, 0.03609824180603027f,
-0.1587022840976715f, 0.059984564781188965f, -0.03951650857925415f,
-0.4841443598270416f, 0.32919085025787354f, -0.23115816712379456f,
0.39441078901290894f, -0.3554944396018982f, -0.17022761702537537f,
-0.055081307888031006f, 0.15856128931045532f, -0.4183449149131775f,
-0.2474445104598999f, 0.03603637218475342f, -0.2836887538433075f,
0.4602506160736084f, 0.29092925786972046f, -0.199321448802948f,
0.380856454372406f, -0.13847029209136963f, -0.238397479057312f,
-0.1907123327255249f, -0.11061936616897583f, -0.08717870712280273f,
0.24449139833450317f, -0.14727482199668884f, 0.1437196135520935f,
0.3955056071281433f, -0.12538021802902222f, 0.11590522527694702f,
0.4598066806793213f, -0.30005723237991333f, -0.46578651666641235f,
-0.33955082297325134f, -0.2671887278556824f, 0.3611910939216614f,
-0.11423084139823914f, -0.08382436633110046f, -0.31819307804107666f,
0.14515334367752075f, 0.3157258629798889f, 0.33179205656051636f,
-0.2558857202529907f, 0.11888682842254639f, 0.12824326753616333f,
-0.33106181025505066f, 0.2549159526824951f, -0.46760573983192444f,
-0.11983257532119751f, 0.1834418773651123f});
// W: {2, 1, 2, 2, 2}
inputs.emplace_back(std::vector<float>{0.388077974319458f,
-0.16366064548492432f,
-0.42871910333633423f,
0.4276432394981384f,
0.21517693996429443f,
0.007908165454864502f,
0.33897721767425537f,
0.21843165159225464f,
0.34095364809036255f,
-0.17043980956077576f,
-0.013571739196777344f,
-0.26793742179870605f,
-0.34863436222076416f,
-0.2672275900840759f,
-0.36691007018089294f,
0.37296557426452637f});
// B: {2}
inputs.emplace_back(std::vector<float>{0.4310183525085449f, -0.4564093053340912f});
// {2, 2, 3, 3, 3}
Outputs expected_output{std::vector<float>{
0.5332361459732056f, 0.6628494262695312f, 0.544619083404541f, 0.4242798388004303f,
0.6271085739135742f, 0.6721994876861572f, 0.43064039945602417f, 0.4246789515018463f,
0.53834068775177f, 0.6932926177978516f, 0.42797625064849854f, 0.2218741625547409f,
0.29522019624710083f, 0.8329390287399292f, 0.37605351209640503f, 0.43735477328300476f,
0.2920728623867035f, 0.6692450046539307f, 0.5527016520500183f, 0.22643595933914185f,
0.5138190984725952f, 0.3041342794895172f, 0.7423423528671265f, 0.26707080006599426f,
0.4617553651332855f, 0.32416003942489624f, 0.511577844619751f, -0.28187549114227295f,
-0.5031181573867798f, -0.5793710947036743f, -0.5992864370346069f, -0.5055556893348694f,
-0.7562476396560669f, -0.44363799691200256f, -0.5730307102203369f, -0.6302952766418457f,
-0.4756688177585602f, -0.728988528251648f, -0.3900943398475647f, -0.6694478988647461f,
-0.38822290301322937f, -0.35774707794189453f, -0.39807581901550293f, -0.547709047794342f,
-0.35872578620910645f, -0.5326492786407471f, -0.40852290391921997f, -0.4537881314754486f,
-0.4545857608318329f, -0.379546195268631f, -0.5250767469406128f, -0.42439910769462585f,
-0.5558245182037354f, -0.38563215732574463f, 0.44995537400245667f, 0.5007325410842896f,
0.49359965324401855f, 0.40685802698135376f, 0.407518208026886f, 0.4628955125808716f,
0.4301188290119171f, 0.40635955333709717f, 0.4260363280773163f, 0.55128413438797f,
0.5498291254043579f, 0.27105778455734253f, 0.40259143710136414f, 0.5747092962265015f,
0.4187920391559601f, 0.4507707953453064f, 0.420598566532135f, 0.3950541913509369f,
0.593889057636261f, 0.16578882932662964f, 0.5332239270210266f, 0.43014785647392273f,
0.50260329246521f, 0.39225444197654724f, 0.4074971079826355f, 0.5073125958442688f,
0.3823610544204712f, -0.4240749180316925f, -0.41936254501342773f, -0.5241475105285645f,
-0.5220003724098206f, -0.502869725227356f, -0.5122783780097961f, -0.4260129928588867f,
-0.4105660617351532f, -0.4483373165130615f, -0.33759188652038574f, -0.735706090927124f,
-0.3714444637298584f, -0.4888814687728882f, -0.6191370487213135f, -0.2640320658683777f,
-0.47542816400527954f, -0.5078460574150085f, -0.4205915927886963f, -0.5584549903869629f,
-0.39770257472991943f, -0.45317384600639343f, -0.5598302483558655f, -0.2542789578437805f,
-0.5359901785850525f, -0.48090484738349915f, -0.38603779673576355f, -0.4991581439971924f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_matmul_vec_ten3d)
{
auto function = onnx_import::import_onnx_model(
......@@ -1647,25 +1236,6 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_sum_opset8)
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_conv_transpose_w_groups)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/conv_transpose_w_groups.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{
0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f});
inputs.emplace_back(std::vector<float>{0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f,
8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f,
16.f, 17.f, 18.f, 19.f, 20.f, 21.f, 22.f, 23.f,
24.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 31.0f});
Outputs expected_output{
std::vector<float>{28.f, 34.f, 252.f, 274.f, 732.f, 770.f, 1468.f, 1522.f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_argmax_int32)
{
auto function = onnx_import::import_onnx_model(
......@@ -1697,361 +1267,54 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_argmin_int32)
EXPECT_TRUE(test::all_close(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, is_op_supported)
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_top_k)
{
// Simple case
EXPECT_TRUE(onnx_import::is_operator_supported("Sum", 1, "ai.onnx"));
// With fallback
EXPECT_TRUE(onnx_import::is_operator_supported("Sum", 100, "ai.onnx"));
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/top_k.prototxt"));
// Different opset versions
EXPECT_TRUE(onnx_import::is_operator_supported("Add", 1, "ai.onnx"));
EXPECT_TRUE(onnx_import::is_operator_supported("Add", 7, "ai.onnx"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11});
// Default domain name
EXPECT_TRUE(onnx_import::is_operator_supported("Sum", 1));
std::vector<float> expected_values_output{3, 2, 1, 7, 6, 5, 11, 10, 9};
std::vector<std::int64_t> expected_indices_output{3, 2, 1, 3, 2, 1, 3, 2, 1};
// Unregistered operator
EXPECT_FALSE(onnx_import::is_operator_supported("DummyOp", 1));
EXPECT_FALSE(onnx_import::is_operator_supported("DummyOp", 1, "ai.onnx"));
EXPECT_FALSE(onnx_import::is_operator_supported("DummyOp", 10, "ai.onnx"));
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors =
prepare_and_run(function, inputs, "${BACKEND_NAME}");
// Operator with bad domain name
EXPECT_FALSE(onnx_import::is_operator_supported("Sum", 1, "bad.domain"));
std::vector<float> values_output = read_vector<float>(result_tensors.at(0));
std::vector<std::int64_t> indices_output = read_vector<std::int64_t>(result_tensors.at(1));
// Registered custom operator
onnx_import::register_operator(
"AddQ", 1, "com.intel.ai", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
return {std::make_shared<ngraph::op::Add>(ng_inputs.at(0), ng_inputs.at(1))};
});
EXPECT_TRUE(onnx_import::is_operator_supported("AddQ", 1, "com.intel.ai"));
EXPECT_TRUE(test::all_close_f(expected_values_output, values_output));
EXPECT_TRUE(test::all_close(expected_indices_output, indices_output));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_depth_to_space)
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_sinh)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/depth_to_space.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{
0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f});
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/sinh.prototxt"));
Outputs expected_output{std::vector<float>{
0.f, 4.f, 1.f, 5.f, 8.f, 12.f, 9.f, 13.f, 2.f, 6.f, 3.f, 7.f, 10.f, 14.f, 11.f, 15.f}};
Inputs inputs{std::vector<float>{-1.0f, 0.0f, 1.0f}};
Outputs expected_outputs{std::vector<float>{-1.1752012f, 0.f, 1.1752012f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_depth_to_space_chw)
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_cosh)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/depth_to_space_chw.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{
0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f});
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/cosh.prototxt"));
Outputs expected_output{std::vector<float>{
0.f, 4.f, 1.f, 5.f, 8.f, 12.f, 9.f, 13.f, 2.f, 6.f, 3.f, 7.f, 10.f, 14.f, 11.f, 15.f}};
Inputs inputs{std::vector<float>{-1.0f, 0.0f, 1.0f}};
Outputs expected_outputs{std::vector<float>{1.54308069f, 1.f, 1.54308069f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_depth_to_space_bad_blocksize)
{
// This model fails to import since the depth channel length must be a multiple of the
// `blocksize` attribute value.
EXPECT_THROW(onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/depth_to_space_bad_blocksize.prototxt")),
std::runtime_error);
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_depth_to_space_no_blocksize)
{
// This model fails to import since it lacks of required parameter `blocksize`.
EXPECT_THROW(onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/depth_to_space_no_blocksize.prototxt")),
std::runtime_error);
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_space_to_depth)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/space_to_depth.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f,
8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f,
16.f, 17.f, 18.f, 19.f, 20.f, 21.f, 22.f, 23.f,
24.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 31.f});
Outputs expected_output{std::vector<float>{
0.f, 2.f, 8.f, 10.f, 16.f, 18.f, 24.f, 26.f, 1.f, 3.f, 9.f, 11.f, 17.f, 19.f, 25.f, 27.f,
4.f, 6.f, 12.f, 14.f, 20.f, 22.f, 28.f, 30.f, 5.f, 7.f, 13.f, 15.f, 21.f, 23.f, 29.f, 31.f,
}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_space_to_depth_chw)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/space_to_depth_chw.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f,
8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f,
16.f, 17.f, 18.f, 19.f, 20.f, 21.f, 22.f, 23.f,
24.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 31.f});
Outputs expected_output{std::vector<float>{
0.f, 2.f, 8.f, 10.f, 16.f, 18.f, 24.f, 26.f, 1.f, 3.f, 9.f, 11.f, 17.f, 19.f, 25.f, 27.f,
4.f, 6.f, 12.f, 14.f, 20.f, 22.f, 28.f, 30.f, 5.f, 7.f, 13.f, 15.f, 21.f, 23.f, 29.f, 31.f,
}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_space_to_depth_bad_blocksize)
{
// This model fails to import since the depth channel length must be a multiple of the
// `blocksize` attribute value.
EXPECT_THROW(onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/space_to_depth_bad_blocksize.prototxt")),
std::runtime_error);
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_space_to_depth_no_blocksize)
{
// This model fails to import since it lacks of required `blocksize` attribute.
EXPECT_THROW(onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/space_to_depth_no_blocksize.prototxt")),
std::runtime_error);
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_missing_op_domain)
{
onnx_import::register_operator(
"CustomAdd", 1, "custom.op", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
return {std::make_shared<ngraph::op::Add>(ng_inputs.at(0), ng_inputs.at(1))};
});
EXPECT_TRUE(onnx_import::is_operator_supported("CustomAdd", 1, "custom.op"));
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/missing_op_domain.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{0.f, 1.f, 2.f, 3.f});
inputs.emplace_back(std::vector<float>{0.f, 1.f, 2.f, 3.f});
Outputs expected_output{std::vector<float>{0.f, 2.f, 4.f, 6.f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_top_k)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/top_k.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11});
std::vector<float> expected_values_output{3, 2, 1, 7, 6, 5, 11, 10, 9};
std::vector<std::int64_t> expected_indices_output{3, 2, 1, 3, 2, 1, 3, 2, 1};
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors =
prepare_and_run(function, inputs, "${BACKEND_NAME}");
std::vector<float> values_output = read_vector<float>(result_tensors.at(0));
std::vector<std::int64_t> indices_output = read_vector<std::int64_t>(result_tensors.at(1));
EXPECT_TRUE(test::all_close_f(expected_values_output, values_output));
EXPECT_TRUE(test::all_close(expected_indices_output, indices_output));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_lstm_fwd_with_clip)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/lstm_fwd_with_clip.prototxt"));
Inputs inputs{};
// X
inputs.emplace_back(std::vector<float>{-0.455351, -0.276391, -0.185934, -0.269585});
// W
inputs.emplace_back(std::vector<float>{-0.494659f,
0.0453352f,
-0.487793f,
0.417264f,
-0.0175329f,
0.489074f,
-0.446013f,
0.414029f,
-0.0091708f,
-0.255364f,
-0.106952f,
-0.266717f,
-0.0888852f,
-0.428709f,
-0.283349f,
0.208792f});
// R
inputs.emplace_back(std::vector<float>{0.146626f,
-0.0620289f,
-0.0815302f,
0.100482f,
-0.219535f,
-0.306635f,
-0.28515f,
-0.314112f,
-0.228172f,
0.405972f,
0.31576f,
0.281487f,
-0.394864f,
0.42111f,
-0.386624f,
-0.390225f});
// B
inputs.emplace_back(std::vector<float>{0.381619f,
0.0323954f,
-0.14449f,
0.420804f,
-0.258721f,
0.45056f,
-0.250755f,
0.0967895f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f});
// P
inputs.emplace_back(std::vector<float>{0.2345f, 0.5235f, 0.4378f, 0.3475f, 0.8927f, 0.3456f});
Outputs expected_output{};
// Y_data
expected_output.emplace_back(
std::vector<float>{-0.02280854f, 0.02744377f, -0.03516197f, 0.03875681f});
// Y_h_data
expected_output.emplace_back(std::vector<float>{-0.03516197f, 0.03875681f});
// Y_c_data
expected_output.emplace_back(std::vector<float>{-0.07415761f, 0.07395997f});
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(outputs.size() == expected_output.size());
for (std::size_t i{0}; i < expected_output.size(); ++i)
{
// We have to enlarge tolerance bits to 3 - it's only one bit more than default value.
// The discrepancies may occur at most on 7th decimal position.
EXPECT_TRUE(test::all_close_f(expected_output.at(i), outputs.at(i), 3));
}
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_missing_input)
{
onnx_import::register_operator(
"TestMissingInOut", 1, "com.intel.ai", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
std::shared_ptr<ngraph::Node> A = ng_inputs.at(0);
std::shared_ptr<ngraph::Node> B = ng_inputs.at(1);
std::shared_ptr<ngraph::Node> C = ng_inputs.at(2);
A = A * C;
if (!B->is_null())
{
B = B / C;
}
C = C + C;
return {A, B, C};
});
onnx_import::register_operator(
"TestMissingIn", 1, "com.intel.ai", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
std::shared_ptr<ngraph::Node> result = std::make_shared<ngraph::op::Constant>(
element::f32, ngraph::Shape{2, 2}, std::vector<float>{1, 1, 1, 1});
for (const auto& ng_input : ng_inputs)
{
if (!ng_input->is_null())
{
result = ng_input * result;
}
}
return {result};
});
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/missing_input.prototxt"));
Inputs inputs{{1, 2, 3, 4}, {5, 6, 7, 8}};
Outputs expected_outputs{{50, 144, 294, 512}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_sinh)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/sinh.prototxt"));
Inputs inputs{std::vector<float>{-1.0f, 0.0f, 1.0f}};
Outputs expected_outputs{std::vector<float>{-1.1752012f, 0.f, 1.1752012f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_cosh)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/cosh.prototxt"));
Inputs inputs{std::vector<float>{-1.0f, 0.0f, 1.0f}};
Outputs expected_outputs{std::vector<float>{1.54308069f, 1.f, 1.54308069f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_initializer_wo_input)
{
// This test checks a model which has an initializer, but no input with the same name
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/initializer_wo_input.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{0, 1, 2, 3, 4, 5});
std::vector<float> expected_output{0, 2, 6, 12, 20, 30};
Outputs output{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output, output.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_sign)
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_sign)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/sign.prototxt"));
......@@ -2069,65 +1332,6 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_sign)
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_global_lp_pool_p0)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/global_lp_pool_p0.prototxt"));
std::vector<std::vector<std::int64_t>> inputs{std::vector<std::int64_t>{
1, 0, -4, 0, 2, 1, -6, 1, 0, 0, 0, 0, -7, 1, -1, 0, -1, 8, 0, 10, 9, 0, 0, 5}};
std::vector<std::vector<std::int64_t>> expected_outputs{std::vector<std::int64_t>{6, 8}};
std::vector<std::vector<std::int64_t>> outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_global_lp_pool_p1)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/global_lp_pool_p1.prototxt"));
Inputs inputs{std::vector<float>(2 * 3 * 4)};
std::iota(std::begin(inputs.front()), std::end(inputs.front()), 0.f);
Outputs expected_outputs{std::vector<float>{66.f, 210.f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_global_lp_pool_p2)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/global_lp_pool_p2.prototxt"));
Inputs inputs{std::vector<float>(2 * 3 * 4)};
std::iota(std::begin(inputs.front()), std::end(inputs.front()), 0.f);
Outputs expected_outputs{std::vector<float>{22.494444f, 61.789967f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_global_lp_pool_p3)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/global_lp_pool_p3.prototxt"));
Inputs inputs{std::vector<float>(2 * 3 * 4)};
std::iota(std::begin(inputs.front()), std::end(inputs.front()), 0.f);
Outputs expected_outputs{std::vector<float>{16.331620904278438f, 41.56697946707537f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_one_hot_with_axis)
{
auto function = onnx_import::import_onnx_model(
......@@ -2186,649 +1390,6 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_where)
EXPECT_EQ(expected_outputs.front(), outputs.front());
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_override_op)
{
onnx_import::register_operator(
"FalseAdd", 1, "", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
return {std::make_shared<ngraph::op::Add>(ng_inputs.at(0), ng_inputs.at(1))};
});
onnx_import::register_operator(
"FalseAdd", 1, "", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
return {std::make_shared<ngraph::op::Subtract>(ng_inputs.at(0), ng_inputs.at(1))};
});
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/override_op.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{0.f, 1.f, 2.f, 3.f});
inputs.emplace_back(std::vector<float>{3.f, 2.f, 1.f, 0.f});
Outputs expected_output{std::vector<float>{-3.f, -1.f, 1.f, 3.f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, import_non_existing_file)
{
try
{
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/i.dont.exist"));
}
catch (const std::runtime_error& exc)
{
// asserts that an exception was thrown and that the error message contains the file name
std::string msg{exc.what()};
EXPECT_TRUE(msg.find("i.dont.exist") != std::string::npos);
}
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_lstm_fwd_mixed_seq)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/lstm_fwd_mixed_seq.prototxt"));
int hidden_size{3};
int parameters_cout{5};
// X
std::vector<float> in_x{1.f, 2.f, 10.f, 11.f};
// W
std::vector<float> in_w{0.1f, 0.2f, 0.3f, 0.4f, 1.f, 2.f, 3.f, 4.f, 10.f, 11.f, 12.f, 13.f};
// R
std::vector<float> in_r(4 * hidden_size * hidden_size, 0.1f);
// B
std::vector<float> in_b(8 * hidden_size, 0.0f);
std::vector<int> in_seq_lengths{1, 2};
std::vector<float> out_y_data{0.28828835f,
0.36581863f,
0.45679406f,
0.34526032f,
0.47220859f,
0.55850911f,
0.f,
0.f,
0.f,
0.85882828f,
0.90703777f,
0.92382453f};
std::vector<float> out_y_h_data{
0.28828835f, 0.36581863f, 0.45679406f, 0.85882828f, 0.90703777f, 0.92382453f};
std::vector<float> out_y_c_data{
0.52497941f, 0.54983425f, 0.5744428f, 1.3249796f, 1.51063104f, 1.61451544f};
Outputs expected_output;
expected_output.emplace_back(out_y_data);
expected_output.emplace_back(out_y_h_data);
expected_output.emplace_back(out_y_c_data);
auto backend = ngraph::runtime::Backend::create("${BACKEND_NAME}");
auto parameters = function->get_parameters();
EXPECT_TRUE(parameters.size() == parameters_cout);
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> arg_tensors;
auto add_tensor = [&arg_tensors, &backend](const std::vector<float>& v,
const std::shared_ptr<ngraph::op::Parameter>& p) {
auto t = backend->create_tensor(p->get_element_type(), p->get_shape());
copy_data(t, v);
arg_tensors.push_back(t);
};
add_tensor(in_x, parameters.at(0));
add_tensor(in_w, parameters.at(1));
add_tensor(in_r, parameters.at(2));
add_tensor(in_b, parameters.at(3));
auto t_in_seq_lengths =
backend->create_tensor(parameters.at(4)->get_element_type(), parameters.at(4)->get_shape());
copy_data(t_in_seq_lengths, in_seq_lengths);
arg_tensors.push_back(t_in_seq_lengths);
auto results = function->get_results();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors(results.size());
for (std::size_t i{0}; i < results.size(); ++i)
{
result_tensors.at(i) =
backend->create_tensor(results.at(i)->get_element_type(), results.at(i)->get_shape());
}
auto handle = backend->compile(function);
handle->call_with_validate(result_tensors, arg_tensors);
Outputs outputs;
for (auto rt : result_tensors)
{
outputs.push_back(read_vector<float>(rt));
}
EXPECT_TRUE(outputs.size() == expected_output.size());
for (std::size_t i{0}; i < expected_output.size(); ++i)
{
// We have to enlarge tolerance bits to 3 - it's only one bit more than default value.
// The discrepancies may occur at most on 7th decimal position.
EXPECT_TRUE(test::all_close_f(expected_output.at(i), outputs.at(i), 3));
}
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_quantize_linear)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/quantize_linear.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{32.25f, 48.34f, 50.f, 83.f});
inputs.emplace_back(std::vector<float>{0.5f});
std::vector<std::vector<std::uint8_t>> expected_output{
std::vector<std::uint8_t>{64, 97, 100, 166}};
std::vector<std::vector<std::uint8_t>> outputs{
execute<float, std::uint8_t>(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_quantize_linear_zero_point)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/quantize_linear_zero_point.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{0.f, 2.f, 3.f, 1000.f, -254.f, -1000.f}); // x
inputs.emplace_back(std::vector<float>{2.0f}); // y_scale
std::vector<std::vector<std::uint8_t>> int_inputs;
int_inputs.emplace_back(std::vector<std::uint8_t>{128}); // y_zero_point
std::vector<std::vector<std::uint8_t>> expected_output{
std::vector<std::uint8_t>{128, 129, 130, 255, 1, 0}};
std::vector<std::vector<std::uint8_t>> outputs{execute<float, std::uint8_t, std::uint8_t>(
function, inputs, int_inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, quantize_linear_axis_zero)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/quantize_linear_axis_zero.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{
0.f, 2.f, 3.f, 1000.f, 0.f, 2.f, 3.f, 1000.f, 0.f, 2.f, 3.f, 1000.f}); // x
inputs.emplace_back(std::vector<float>{1.f, 2.f, 4.f}); // y_scale
std::vector<std::vector<std::uint8_t>> int_inputs;
int_inputs.emplace_back(std::vector<std::uint8_t>{0, 0, 0}); // y_zero_point
std::vector<std::vector<std::uint8_t>> expected_output{
// std::vector<std::uint8_t>{0, 2, 3, 255, 0, 1, 2, 255, 0, 1, 1, 250}}; <- bad expected output given HALF_TO_EVEN round mode
std::vector<std::uint8_t>{0, 2, 3, 255, 0, 1, 2, 255, 0, 0, 1, 250}};
std::vector<std::vector<std::uint8_t>> outputs{execute<float, std::uint8_t, std::uint8_t>(
function, inputs, int_inputs, "${BACKEND_NAME}")};
EXPECT_EQ(expected_output.front(), outputs.front());
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_quantize_linear_axis_negative)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/quantize_linear_axis_negative.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{
0.f, 2.f, 3.f, 1000.f, 0.f, 2.f, 3.f, 1000.f, 0.f, 2.f, 3.f, 1000.f}); // x
inputs.emplace_back(std::vector<float>{1.f, 2.f, 4.f}); // y_scale
std::vector<std::vector<std::uint8_t>> int_inputs;
int_inputs.emplace_back(std::vector<std::uint8_t>{0, 0, 0}); // y_zero_point
std::vector<std::vector<std::uint8_t>> expected_output{
// std::vector<std::uint8_t>{0, 2, 3, 255, 0, 1, 2, 255, 0, 1, 1, 250}}; <- bad expected output given HALF_TO_EVEN round mode
std::vector<std::uint8_t>{0, 2, 3, 255, 0, 1, 2, 255, 0, 0, 1, 250}};
std::vector<std::vector<std::uint8_t>> outputs{execute<float, std::uint8_t, std::uint8_t>(
function, inputs, int_inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_dequantize_linear)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/dequant_lin.prototxt"));
std::vector<std::vector<std::uint8_t>> inputs;
inputs.emplace_back(std::vector<std::uint8_t>{19, 210, 21, 10});
Outputs expected_output{std::vector<float>{76.f, 840.f, 84.f, 40.f}};
Outputs outputs{execute<std::uint8_t, float>(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_dequantize_linear_scalar_zero_scale_uint8)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/dequantize_linear_0.prototxt"));
auto x = std::vector<uint8_t>{0, 3, 128, 255};
auto scale = std::vector<float>{2.0f};
auto zero_point = std::vector<uint8_t>{128};
auto backend = ngraph::runtime::Backend::create("${BACKEND_NAME}");
auto params = function->get_parameters();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> input_tensors;
input_tensors.push_back(
backend->create_tensor(params.at(0)->get_element_type(), params.at(0)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(1)->get_element_type(), params.at(1)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(2)->get_element_type(), params.at(2)->get_shape()));
copy_data(input_tensors[0], x);
copy_data(input_tensors[1], scale);
copy_data(input_tensors[2], zero_point);
auto results = function->get_results();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors;
result_tensors.push_back(
backend->create_tensor(results.at(0)->get_element_type(), results.at(0)->get_shape()));
auto handle = backend->compile(function);
handle->call_with_validate(result_tensors, input_tensors);
std::vector<std::vector<float>> outputs;
outputs.push_back(read_vector<float>(result_tensors[0]));
auto expected_output = std::vector<std::vector<float>>{{-256.0f, -250.0f, 0.0f, 254.0f}};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_dequantize_linear_scalar_zero_scale_int8)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/dequantize_linear_1.prototxt"));
auto x = std::vector<int8_t>{-30, -3, 100, 127};
auto scale = std::vector<float>{2.0f};
auto zero_point = std::vector<int8_t>{-10};
auto backend = ngraph::runtime::Backend::create("${BACKEND_NAME}");
auto params = function->get_parameters();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> input_tensors;
input_tensors.push_back(
backend->create_tensor(params.at(0)->get_element_type(), params.at(0)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(1)->get_element_type(), params.at(1)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(2)->get_element_type(), params.at(2)->get_shape()));
copy_data(input_tensors[0], x);
copy_data(input_tensors[1], scale);
copy_data(input_tensors[2], zero_point);
auto results = function->get_results();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors;
result_tensors.push_back(
backend->create_tensor(results.at(0)->get_element_type(), results.at(0)->get_shape()));
auto handle = backend->compile(function);
handle->call_with_validate(result_tensors, input_tensors);
std::vector<std::vector<float>> outputs;
outputs.push_back(read_vector<float>(result_tensors[0]));
auto expected_output = std::vector<std::vector<float>>{{-40.0f, 14.0f, 220.0f, 274.0f}};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_dequantize_linear_1d_zero_scale_uint8)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/dequantize_linear_2.prototxt"));
auto x = std::vector<uint8_t>{0, 1, 2, 3, 0, 1, 2, 3, 0, 10, 20, 30};
auto scale = std::vector<float>{1.0f, 2.0f, 4.0f};
auto zero_point = std::vector<uint8_t>{0, 0, 0};
auto backend = ngraph::runtime::Backend::create("${BACKEND_NAME}");
auto params = function->get_parameters();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> input_tensors;
input_tensors.push_back(
backend->create_tensor(params.at(0)->get_element_type(), params.at(0)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(1)->get_element_type(), params.at(1)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(2)->get_element_type(), params.at(2)->get_shape()));
copy_data(input_tensors[0], x);
copy_data(input_tensors[1], scale);
copy_data(input_tensors[2], zero_point);
auto results = function->get_results();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors;
result_tensors.push_back(
backend->create_tensor(results.at(0)->get_element_type(), results.at(0)->get_shape()));
auto handle = backend->compile(function);
handle->call_with_validate(result_tensors, input_tensors);
std::vector<std::vector<float>> outputs;
outputs.push_back(read_vector<float>(result_tensors[0]));
auto expected_output = std::vector<std::vector<float>>{
{0.0f, 1.0f, 2.0f, 3.0f, 0.0f, 2.0f, 4.0f, 6.0f, 0.0f, 40.0f, 80.0f, 120.0f}};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_dequantize_linear_1d_zero_scale_int8)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/dequantize_linear_3.prototxt"));
auto x = std::vector<int8_t>{0, 1, 2, 3, 0, 2, 4, 6, 0, 10, 20, 30};
auto scale = std::vector<float>{1.0f, 2.0f, 4.0f, 8.0f};
auto zero_point = std::vector<int8_t>{0, -10, -20, -30};
auto backend = ngraph::runtime::Backend::create("${BACKEND_NAME}");
auto params = function->get_parameters();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> input_tensors;
input_tensors.push_back(
backend->create_tensor(params.at(0)->get_element_type(), params.at(0)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(1)->get_element_type(), params.at(1)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(2)->get_element_type(), params.at(2)->get_shape()));
copy_data(input_tensors[0], x);
copy_data(input_tensors[1], scale);
copy_data(input_tensors[2], zero_point);
auto results = function->get_results();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors;
result_tensors.push_back(
backend->create_tensor(results.at(0)->get_element_type(), results.at(0)->get_shape()));
auto handle = backend->compile(function);
handle->call_with_validate(result_tensors, input_tensors);
std::vector<std::vector<float>> outputs;
outputs.push_back(read_vector<float>(result_tensors[0]));
auto expected_output = std::vector<std::vector<float>>{
{0.0f, 22.0f, 88.0f, 264.0f, 0.0f, 24.0f, 96.0f, 288.0f, 0.0f, 40.0f, 160.0f, 480.0f}};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_dequantize_linear_1d_zero_scale_int8_4d)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/dequantize_linear_4.prototxt"));
auto x = std::vector<int8_t>{7, 9, 10, 10, 5, 8, 9, 1, 8, 6, 7, 9, 10, 0, 7, 10,
8, 2, 6, 0, 5, 9, 8, 1, 2, 7, 5, 3, 2, 4, 1, 3,
8, 7, 4, 8, 10, 1, 5, 5, 7, 7, 0, 2, 4, 4, 0, 5};
auto scale = std::vector<float>{1.0f, 10.0f, 7.0f};
auto zero_point = std::vector<int8_t>{10, 2, 1};
auto backend = ngraph::runtime::Backend::create("${BACKEND_NAME}");
auto params = function->get_parameters();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> input_tensors;
input_tensors.push_back(
backend->create_tensor(params.at(0)->get_element_type(), params.at(0)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(1)->get_element_type(), params.at(1)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(2)->get_element_type(), params.at(2)->get_shape()));
copy_data(input_tensors[0], x);
copy_data(input_tensors[1], scale);
copy_data(input_tensors[2], zero_point);
auto results = function->get_results();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors;
result_tensors.push_back(
backend->create_tensor(results.at(0)->get_element_type(), results.at(0)->get_shape()));
auto handle = backend->compile(function);
handle->call_with_validate(result_tensors, input_tensors);
std::vector<std::vector<float>> outputs;
outputs.push_back(read_vector<float>(result_tensors[0]));
auto expected_output = std::vector<std::vector<float>>{
{-3.0f, -1.0f, 0.0f, 0.0f, -5.0f, -2.0f, -1.0f, -9.0f, 60.0f, 40.0f, 50.0f, 70.0f,
80.0f, -20.0f, 50.0f, 80.0f, 49.0f, 7.0f, 35.0f, -7.0f, 28.0f, 56.0f, 49.0f, 0.0f,
-8.0f, -3.0f, -5.0f, -7.0f, -8.0f, -6.0f, -9.0f, -7.0f, 60.0f, 50.0f, 20.0f, 60.0f,
80.0f, -10.0f, 30.0f, 30.0f, 42.0f, 42.0f, -7.0f, 7.0f, 21.0f, 21.0f, -7.0f, 28.0f}};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_dequantize_linear_1d_zero_scale_uint8_negative_axis)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/dequantize_linear_5.prototxt"));
auto x = std::vector<uint8_t>{0, 1, 2, 3, 0, 1, 2, 3, 0, 10, 20, 30};
auto scale = std::vector<float>{1.0f, 2.0f, 4.0f};
auto zero_point = std::vector<uint8_t>{0, 0, 0};
auto backend = ngraph::runtime::Backend::create("${BACKEND_NAME}");
auto params = function->get_parameters();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> input_tensors;
input_tensors.push_back(
backend->create_tensor(params.at(0)->get_element_type(), params.at(0)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(1)->get_element_type(), params.at(1)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(2)->get_element_type(), params.at(2)->get_shape()));
copy_data(input_tensors[0], x);
copy_data(input_tensors[1], scale);
copy_data(input_tensors[2], zero_point);
auto results = function->get_results();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors;
result_tensors.push_back(
backend->create_tensor(results.at(0)->get_element_type(), results.at(0)->get_shape()));
auto handle = backend->compile(function);
handle->call_with_validate(result_tensors, input_tensors);
std::vector<std::vector<float>> outputs;
outputs.push_back(read_vector<float>(result_tensors[0]));
auto expected_output = std::vector<std::vector<float>>{
{0.0f, 1.0f, 2.0f, 3.0f, 0.0f, 2.0f, 4.0f, 6.0f, 0.0f, 40.0f, 80.0f, 120.0f}};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_quant_conv_linear)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/quant_conv_lin.prototxt"));
std::vector<std::vector<std::uint8_t>> inputs;
inputs.emplace_back(std::vector<std::uint8_t>{
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81});
std::vector<std::vector<std::int8_t>> expected_output{std::vector<std::int8_t>{
2, 3, 3, 3, 4, 4, 4, 5, 2, 4, 6, 7, 8, 8, 9, 9, 10, 3, 8, 11, 12,
13, 13, 14, 14, 15, 5, 11, 16, 17, 18, 18, 19, 19, 20, 7, 14, 22, 22, 23, 23, 24,
24, 25, 8, 18, 27, 27, 28, 28, 29, 29, 30, 10, 21, 32, 32, 33, 33, 34, 34, 35, 12,
24, 37, 37, 38, 38, 39, 40, 40, 13, 17, 26, 27, 27, 27, 28, 28, 28, 9}};
std::vector<std::vector<std::int8_t>> outputs{
execute<std::uint8_t, std::int8_t>(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_quant_conv_linear_2d)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/qlinear_conv_2d.prototxt"));
auto x =
read_binary_file<uint8_t>(file_util::path_join(TEST_FILES, "onnx/qlinearconv2d/x.bin"));
auto x_scale =
read_binary_file<float>(file_util::path_join(TEST_FILES, "onnx/qlinearconv2d/x_scale.bin"));
auto x_zero_point = read_binary_file<uint8_t>(
file_util::path_join(TEST_FILES, "onnx/qlinearconv2d/x_zero_point.bin"));
auto w =
read_binary_file<uint8_t>(file_util::path_join(TEST_FILES, "onnx/qlinearconv2d/w.bin"));
auto w_scale =
read_binary_file<float>(file_util::path_join(TEST_FILES, "onnx/qlinearconv2d/w_scale.bin"));
auto w_zero_point = read_binary_file<uint8_t>(
file_util::path_join(TEST_FILES, "onnx/qlinearconv2d/w_zero_point.bin"));
auto y_scale =
read_binary_file<float>(file_util::path_join(TEST_FILES, "onnx/qlinearconv2d/y_scale.bin"));
auto y_zero_point = read_binary_file<uint8_t>(
file_util::path_join(TEST_FILES, "onnx/qlinearconv2d/y_zero_point.bin"));
auto backend = ngraph::runtime::Backend::create("${BACKEND_NAME}");
auto params = function->get_parameters();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> input_tensors;
input_tensors.push_back(
backend->create_tensor(params.at(0)->get_element_type(), params.at(0)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(1)->get_element_type(), params.at(1)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(2)->get_element_type(), params.at(2)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(3)->get_element_type(), params.at(3)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(4)->get_element_type(), params.at(4)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(5)->get_element_type(), params.at(5)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(6)->get_element_type(), params.at(6)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(7)->get_element_type(), params.at(7)->get_shape()));
copy_data(input_tensors[0], x);
copy_data(input_tensors[1], x_scale);
copy_data(input_tensors[2], x_zero_point);
copy_data(input_tensors[3], w);
copy_data(input_tensors[4], w_scale);
copy_data(input_tensors[5], w_zero_point);
copy_data(input_tensors[6], y_scale);
copy_data(input_tensors[7], y_zero_point);
auto results = function->get_results();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors;
result_tensors.push_back(
backend->create_tensor(results.at(0)->get_element_type(), results.at(0)->get_shape()));
auto handle = backend->compile(function);
handle->call_with_validate(result_tensors, input_tensors);
std::vector<std::vector<uint8_t>> outputs;
outputs.push_back(read_vector<uint8_t>(result_tensors[0]));
std::vector<std::vector<uint8_t>> expected_output;
expected_output.push_back(
read_binary_file<uint8_t>(file_util::path_join(TEST_FILES, "onnx/qlinearconv2d/y.bin")));
EXPECT_EQ(expected_output.front(), outputs.front());
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_quant_conv_linear_3d)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/qlinear_conv_3d.prototxt"));
auto x =
read_binary_file<uint8_t>(file_util::path_join(TEST_FILES, "onnx/qlinearconv3d/x.bin"));
auto x_scale =
read_binary_file<float>(file_util::path_join(TEST_FILES, "onnx/qlinearconv3d/x_scale.bin"));
auto x_zero_point = read_binary_file<uint8_t>(
file_util::path_join(TEST_FILES, "onnx/qlinearconv3d/x_zero_point.bin"));
auto w =
read_binary_file<uint8_t>(file_util::path_join(TEST_FILES, "onnx/qlinearconv3d/w.bin"));
auto w_scale =
read_binary_file<float>(file_util::path_join(TEST_FILES, "onnx/qlinearconv3d/w_scale.bin"));
auto w_zero_point = read_binary_file<uint8_t>(
file_util::path_join(TEST_FILES, "onnx/qlinearconv3d/w_zero_point.bin"));
auto y_scale =
read_binary_file<float>(file_util::path_join(TEST_FILES, "onnx/qlinearconv3d/y_scale.bin"));
auto y_zero_point = read_binary_file<uint8_t>(
file_util::path_join(TEST_FILES, "onnx/qlinearconv3d/y_zero_point.bin"));
auto backend = ngraph::runtime::Backend::create("${BACKEND_NAME}");
auto params = function->get_parameters();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> input_tensors;
input_tensors.push_back(
backend->create_tensor(params.at(0)->get_element_type(), params.at(0)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(1)->get_element_type(), params.at(1)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(2)->get_element_type(), params.at(2)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(3)->get_element_type(), params.at(3)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(4)->get_element_type(), params.at(4)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(5)->get_element_type(), params.at(5)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(6)->get_element_type(), params.at(6)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(7)->get_element_type(), params.at(7)->get_shape()));
copy_data(input_tensors[0], x);
copy_data(input_tensors[1], x_scale);
copy_data(input_tensors[2], x_zero_point);
copy_data(input_tensors[3], w);
copy_data(input_tensors[4], w_scale);
copy_data(input_tensors[5], w_zero_point);
copy_data(input_tensors[6], y_scale);
copy_data(input_tensors[7], y_zero_point);
auto results = function->get_results();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors;
result_tensors.push_back(
backend->create_tensor(results.at(0)->get_element_type(), results.at(0)->get_shape()));
auto handle = backend->compile(function);
handle->call_with_validate(result_tensors, input_tensors);
std::vector<std::vector<uint8_t>> outputs;
outputs.push_back(read_vector<uint8_t>(result_tensors[0]));
std::vector<std::vector<uint8_t>> expected_output;
expected_output.push_back(
read_binary_file<uint8_t>(file_util::path_join(TEST_FILES, "onnx/qlinearconv3d/y.bin")));
EXPECT_EQ(expected_output.front(), outputs.front());
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_erf)
{
const auto function =
......
test/onnx/onnx_import_convpool.in.cpp 0 → 100644
View file @ 57d637bc
//*****************************************************************************
// Copyright 2017-2019 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//*****************************************************************************
#include <algorithm>
#include <cmath>
#include <cstdint>
#include <fstream>
#include <iterator>
#include <limits>
#include <sstream>
#include <stdexcept>
#include <vector>
#include "gtest/gtest.h"
#include "ngraph/frontend/onnx_import/onnx.hpp"
#include "ngraph/ngraph.hpp"
#include "util/all_close.hpp"
#include "util/all_close_f.hpp"
#include "util/ndarray.hpp"
#include "util/test_control.hpp"
#include "util/test_tools.hpp"
using namespace ngraph;
static std::string s_manifest = "${MANIFEST}";
using Inputs = std::vector<std::vector<float>>;
using Outputs = std::vector<std::vector<float>>;
static std::vector<std::vector<float>> conv2d_execute(const std::shared_ptr<Function>& function)
{
std::vector<std::vector<float>> args;
// data (1, 1, 7, 5) input tensor
args.emplace_back(test::NDArray<float, 4>{{{{{0.f, 1.f, 2.f, 3.f, 4.f},
{5.f, 6.f, 7.f, 8.f, 9.f},
{10.f, 11.f, 12.f, 13.f, 14.f},
{15.f, 16.f, 17.f, 18.f, 19.f},
{20.f, 21.f, 22.f, 23.f, 24.f},
{25.f, 26.f, 27.f, 28.f, 29.f},
{30.f, 31.f, 32.f, 33.f, 34.f}}}}}
.get_vector());
// filters (1, 1, 3, 3) aka convolution weights
args.emplace_back(
test::NDArray<float, 4>{{{{{1.f, 1.f, 1.f}, {1.f, 1.f, 1.f}, {1.f, 1.f, 1.f}}}}}
.get_vector());
return execute(function, args, "${BACKEND_NAME}");
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_conv2d_strides_padding)
{
// Convolution with strides=2 and padding=1
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/conv_with_strides_padding.prototxt"));
// (1, 1, 4, 3)
auto expected_output = test::NDArray<float, 4>({{{{12.f, 27.f, 24.f},
{63.f, 108.f, 81.f},
{123.f, 198.f, 141.f},
{112.f, 177.f, 124.f}}}})
.get_vector();
auto result = conv2d_execute(function);
EXPECT_EQ(expected_output, result.front());
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_conv2d_strides_no_padding)
{
// Convolution with strides=2 and padding=1
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/conv_with_strides_no_padding.prototxt"));
// (1, 1, 3, 2)
auto expected_output =
test::NDArray<float, 4>({{{{54.f, 72.f}, {144.f, 162.f}, {234.f, 252.f}}}}).get_vector();
auto result = conv2d_execute(function);
EXPECT_EQ(expected_output, result.front());
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_conv2d_strides_assymetric_padding)
{
// Convolution with strides=2 and padding=1
auto function = onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/conv_with_strides_and_asymmetric_padding.prototxt"));
// (1, 1, 4, 2)
auto expected_output =
test::NDArray<float, 4>({{{{21.f, 33.f}, {99.f, 117.f}, {189.f, 207.f}, {171.f, 183.f}}}})
.get_vector();
auto result = conv2d_execute(function);
EXPECT_EQ(expected_output, result.front());
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_conv2d_dilation_assymetric_pads_strides)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/conv2d_dilation_assym_pads_strides.prototxt"));
// "", // auto_pad
// vector<int64_t>{1, 1}, // dilations
// 1, // group
// vector<int64_t>{3, 3}, // kernel_shape
// vector<int64_t>{1, 1, 1, 2}, // pads
// vector<int64_t>{3, 1} // strides
Inputs inputs;
// {2, 1, 1, 1}
inputs.emplace_back(
test::NDArray<float, 4>({{{{-0.09103918075561523f}}}, {{{-0.32513630390167236f}}}})
.get_vector());
// {2, 1, 3, 3}
inputs.emplace_back(
test::NDArray<float, 4>(
{{{{0.4312484860420227f, -0.12559029459953308f, 0.44889551401138306f},
{-0.3100617825984955f, 0.13522827625274658f, -0.06791308522224426f},
{0.22671669721603394f, -0.17391827702522278f, -0.31299442052841187f}}},
{{{-0.31545522809028625f, 0.06560015678405762f, 0.2656586766242981f},
{0.41363757848739624f, 0.31231558322906494f, -0.376018226146698f},
{-0.005708813667297363f, 0.34922850131988525f, 0.45095211267471313f}}}})
.get_vector());
// {2, 2, 1, 2}
Outputs expected_output{
test::NDArray<float, 4>({{{{-0.012311071157455444f, 0.02822777070105076f}},
{{-0.028432954102754593f, -0.037657227367162704f}}},
{{{-0.04396762326359749f, 0.10081233829259872f}},
{{-0.10154513269662857f, -0.13448859751224518f}}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_conv3d_bias)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/conv3d_bias.prototxt"));
// "", // auto_pad
// vector<int64_t>{2, 2, 2}, // dilations
// 1, // group
// vector<int64_t>{2, 2, 2}, // kernel_shape
// vector<int64_t>{2, 2, 2, 2, 2, 2}, // pads
// vector<int64_t>{2, 2, 2} // strides
Inputs inputs;
// X: {2, 1, 4, 4, 4}
inputs.emplace_back(
std::vector<float>{0.46796226501464844f, -0.4613912105560303f, 0.33512794971466064f,
-0.4010460674762726f, 0.41722816228866577f, -0.048133403062820435f,
0.20415884256362915f, 0.03189706802368164f, -0.04779183864593506f,
-0.0795503556728363f, 0.4987630844116211f, 0.3506373167037964f,
0.48065757751464844f, 0.269855260848999f, -0.2463444471359253f,
0.19044137001037598f, -0.11830493807792664f, -0.2576887905597687f,
-0.33940935134887695f, -0.257951021194458f, -0.08279827237129211f,
0.3513314127922058f, -0.29122066497802734f, -0.43358397483825684f,
-0.13429927825927734f, 0.44032156467437744f, 0.05308258533477783f,
-0.3499870300292969f, -0.28474611043930054f, -0.44209951162338257f,
-0.07418054342269897f, -0.10919415950775146f, 0.2845439314842224f,
0.3498746156692505f, -0.19313520193099976f, 0.32609254121780396f,
0.4880145788192749f, 0.05574071407318115f, -0.46457427740097046f,
-0.02524462342262268f, -0.18780940771102905f, -0.14720159769058228f,
0.207585871219635f, 0.47157740592956543f, -0.05567386746406555f,
-0.49871665239334106f, 0.2274145483970642f, 0.4589425325393677f,
-0.4725189805030823f, -0.4358765780925751f, 0.2841453552246094f,
-0.27037882804870605f, 0.34227508306503296f, 0.33575427532196045f,
-0.19485199451446533f, -0.27679920196533203f, -0.4238079786300659f,
-0.4385119676589966f, 0.43724071979522705f, 0.3065117597579956f,
0.45696544647216797f, 0.05291992425918579f, -0.023618370294570923f,
-0.1860884726047516f, 0.08669537305831909f, 0.32541000843048096f,
0.1846179962158203f, -0.1984834372997284f, -0.2754465937614441f,
0.32004624605178833f, -0.34846532344818115f, 0.0999596118927002f,
-0.11374691128730774f, 0.21225297451019287f, -0.02315312623977661f,
0.1671370267868042f, 0.22319108247756958f, 0.03609824180603027f,
-0.1587022840976715f, 0.059984564781188965f, -0.03951650857925415f,
-0.4841443598270416f, 0.32919085025787354f, -0.23115816712379456f,
0.39441078901290894f, -0.3554944396018982f, -0.17022761702537537f,
-0.055081307888031006f, 0.15856128931045532f, -0.4183449149131775f,
-0.2474445104598999f, 0.03603637218475342f, -0.2836887538433075f,
0.4602506160736084f, 0.29092925786972046f, -0.199321448802948f,
0.380856454372406f, -0.13847029209136963f, -0.238397479057312f,
-0.1907123327255249f, -0.11061936616897583f, -0.08717870712280273f,
0.24449139833450317f, -0.14727482199668884f, 0.1437196135520935f,
0.3955056071281433f, -0.12538021802902222f, 0.11590522527694702f,
0.4598066806793213f, -0.30005723237991333f, -0.46578651666641235f,
-0.33955082297325134f, -0.2671887278556824f, 0.3611910939216614f,
-0.11423084139823914f, -0.08382436633110046f, -0.31819307804107666f,
0.14515334367752075f, 0.3157258629798889f, 0.33179205656051636f,
-0.2558857202529907f, 0.11888682842254639f, 0.12824326753616333f,
-0.33106181025505066f, 0.2549159526824951f, -0.46760573983192444f,
-0.11983257532119751f, 0.1834418773651123f});
// W: {2, 1, 2, 2, 2}
inputs.emplace_back(std::vector<float>{0.388077974319458f,
-0.16366064548492432f,
-0.42871910333633423f,
0.4276432394981384f,
0.21517693996429443f,
0.007908165454864502f,
0.33897721767425537f,
0.21843165159225464f,
0.34095364809036255f,
-0.17043980956077576f,
-0.013571739196777344f,
-0.26793742179870605f,
-0.34863436222076416f,
-0.2672275900840759f,
-0.36691007018089294f,
0.37296557426452637f});
// B: {2}
inputs.emplace_back(std::vector<float>{0.4310183525085449f, -0.4564093053340912f});
// {2, 2, 3, 3, 3}
Outputs expected_output{std::vector<float>{
0.5332361459732056f, 0.6628494262695312f, 0.544619083404541f, 0.4242798388004303f,
0.6271085739135742f, 0.6721994876861572f, 0.43064039945602417f, 0.4246789515018463f,
0.53834068775177f, 0.6932926177978516f, 0.42797625064849854f, 0.2218741625547409f,
0.29522019624710083f, 0.8329390287399292f, 0.37605351209640503f, 0.43735477328300476f,
0.2920728623867035f, 0.6692450046539307f, 0.5527016520500183f, 0.22643595933914185f,
0.5138190984725952f, 0.3041342794895172f, 0.7423423528671265f, 0.26707080006599426f,
0.4617553651332855f, 0.32416003942489624f, 0.511577844619751f, -0.28187549114227295f,
-0.5031181573867798f, -0.5793710947036743f, -0.5992864370346069f, -0.5055556893348694f,
-0.7562476396560669f, -0.44363799691200256f, -0.5730307102203369f, -0.6302952766418457f,
-0.4756688177585602f, -0.728988528251648f, -0.3900943398475647f, -0.6694478988647461f,
-0.38822290301322937f, -0.35774707794189453f, -0.39807581901550293f, -0.547709047794342f,
-0.35872578620910645f, -0.5326492786407471f, -0.40852290391921997f, -0.4537881314754486f,
-0.4545857608318329f, -0.379546195268631f, -0.5250767469406128f, -0.42439910769462585f,
-0.5558245182037354f, -0.38563215732574463f, 0.44995537400245667f, 0.5007325410842896f,
0.49359965324401855f, 0.40685802698135376f, 0.407518208026886f, 0.4628955125808716f,
0.4301188290119171f, 0.40635955333709717f, 0.4260363280773163f, 0.55128413438797f,
0.5498291254043579f, 0.27105778455734253f, 0.40259143710136414f, 0.5747092962265015f,
0.4187920391559601f, 0.4507707953453064f, 0.420598566532135f, 0.3950541913509369f,
0.593889057636261f, 0.16578882932662964f, 0.5332239270210266f, 0.43014785647392273f,
0.50260329246521f, 0.39225444197654724f, 0.4074971079826355f, 0.5073125958442688f,
0.3823610544204712f, -0.4240749180316925f, -0.41936254501342773f, -0.5241475105285645f,
-0.5220003724098206f, -0.502869725227356f, -0.5122783780097961f, -0.4260129928588867f,
-0.4105660617351532f, -0.4483373165130615f, -0.33759188652038574f, -0.735706090927124f,
-0.3714444637298584f, -0.4888814687728882f, -0.6191370487213135f, -0.2640320658683777f,
-0.47542816400527954f, -0.5078460574150085f, -0.4205915927886963f, -0.5584549903869629f,
-0.39770257472991943f, -0.45317384600639343f, -0.5598302483558655f, -0.2542789578437805f,
-0.5359901785850525f, -0.48090484738349915f, -0.38603779673576355f, -0.4991581439971924f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_conv_transpose_w_groups)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/conv_transpose_w_groups.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{
0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f});
inputs.emplace_back(std::vector<float>{0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f,
8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f,
16.f, 17.f, 18.f, 19.f, 20.f, 21.f, 22.f, 23.f,
24.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 31.0f});
Outputs expected_output{
std::vector<float>{28.f, 34.f, 252.f, 274.f, 732.f, 770.f, 1468.f, 1522.f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_average_pool_2d)
{
// Pooling with strides=2 and no padding
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/average_pool_2d.prototxt"));
// input data shape (1, 1, 4, 4)
Inputs inputs;
inputs.push_back(test::NDArray<float, 4>({{{{0.f, 1.f, 2.f, 3.f},
{4.f, 5.f, 6.f, 7.f},
{8.f, 9.f, 10.f, 11.f},
{12.f, 13.f, 14.f, 15.f}}}})
.get_vector());
// (1, 1, 2, 2)
auto expected_output = test::NDArray<float, 4>({{{{2.5f, 4.5f}, {10.5f, 12.5f}}}}).get_vector();
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_EQ(expected_output, outputs.front());
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_average_pool_2d_pads)
{
// Pooling with strides=2 and padding=1
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/average_pool_2d_pads.prototxt"));
// input data shape (1, 1, 4, 4)
Inputs inputs;
inputs.push_back(test::NDArray<float, 4>({{{{0.f, 1.f, 2.f, 3.f},
{4.f, 5.f, 6.f, 7.f},
{8.f, 9.f, 10.f, 11.f},
{12.f, 13.f, 14.f, 15.f}}}})
.get_vector());
// (1, 1, 3, 3)
auto expected_output =
test::NDArray<float, 4>({{{{0.f, 1.5f, 3.f}, {6.f, 7.5f, 9.f}, {12.f, 13.5f, 15.f}}}})
.get_vector();
Outputs outputs = execute(function, inputs, "${BACKEND_NAME}");
EXPECT_EQ(expected_output, outputs.front());
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_max_pool_2d_pads)
{
// Pooling with strides=2 and padding=1
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/max_pool_2d_pads.prototxt"));
// input data shape (1, 1, 4, 4)
Inputs inputs;
inputs.push_back(test::NDArray<float, 4>({{{{0.f, 1.f, 2.f, 3.f},
{4.f, 5.f, 6.f, 7.f},
{8.f, 9.f, 10.f, 11.f},
{12.f, 13.f, 14.f, 15.f}}}})
.get_vector());
// (1, 1, 3, 3)
auto expected_output =
test::NDArray<float, 4>({{{{0.f, 2.f, 3.f}, {8.f, 10.f, 11.f}, {12.f, 14.f, 15.f}}}})
.get_vector();
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_EQ(expected_output, outputs.front());
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_global_lp_pool_p0)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/global_lp_pool_p0.prototxt"));
std::vector<std::vector<std::int64_t>> inputs{std::vector<std::int64_t>{
1, 0, -4, 0, 2, 1, -6, 1, 0, 0, 0, 0, -7, 1, -1, 0, -1, 8, 0, 10, 9, 0, 0, 5}};
std::vector<std::vector<std::int64_t>> expected_outputs{std::vector<std::int64_t>{6, 8}};
std::vector<std::vector<std::int64_t>> outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_global_lp_pool_p1)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/global_lp_pool_p1.prototxt"));
Inputs inputs{std::vector<float>(2 * 3 * 4)};
std::iota(std::begin(inputs.front()), std::end(inputs.front()), 0.f);
Outputs expected_outputs{std::vector<float>{66.f, 210.f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_global_lp_pool_p2)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/global_lp_pool_p2.prototxt"));
Inputs inputs{std::vector<float>(2 * 3 * 4)};
std::iota(std::begin(inputs.front()), std::end(inputs.front()), 0.f);
Outputs expected_outputs{std::vector<float>{22.494444f, 61.789967f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_global_lp_pool_p3)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/global_lp_pool_p3.prototxt"));
Inputs inputs{std::vector<float>(2 * 3 * 4)};
std::iota(std::begin(inputs.front()), std::end(inputs.front()), 0.f);
Outputs expected_outputs{std::vector<float>{16.331620904278438f, 41.56697946707537f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
test/onnx/onnx_import_quant.in.cpp 0 → 100644
View file @ 57d637bc
//*****************************************************************************
// Copyright 2017-2019 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//*****************************************************************************
#include <algorithm>
#include <cmath>
#include <cstdint>
#include <fstream>
#include <iterator>
#include <limits>
#include <sstream>
#include <stdexcept>
#include <vector>
#include "gtest/gtest.h"
#include "ngraph/frontend/onnx_import/onnx.hpp"
#include "ngraph/ngraph.hpp"
#include "util/all_close.hpp"
#include "util/all_close_f.hpp"
#include "util/ndarray.hpp"
#include "util/test_control.hpp"
#include "util/test_tools.hpp"
using namespace ngraph;
static std::string s_manifest = "${MANIFEST}";
using Inputs = std::vector<std::vector<float>>;
using Outputs = std::vector<std::vector<float>>;
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_quantize_linear)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/quantize_linear.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{32.25f, 48.34f, 50.f, 83.f});
inputs.emplace_back(std::vector<float>{0.5f});
std::vector<std::vector<std::uint8_t>> expected_output{
std::vector<std::uint8_t>{64, 97, 100, 166}};
std::vector<std::vector<std::uint8_t>> outputs{
execute<float, std::uint8_t>(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_quantize_linear_zero_point)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/quantize_linear_zero_point.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{0.f, 2.f, 3.f, 1000.f, -254.f, -1000.f}); // x
inputs.emplace_back(std::vector<float>{2.0f}); // y_scale
std::vector<std::vector<std::uint8_t>> int_inputs;
int_inputs.emplace_back(std::vector<std::uint8_t>{128}); // y_zero_point
std::vector<std::vector<std::uint8_t>> expected_output{
std::vector<std::uint8_t>{128, 129, 130, 255, 1, 0}};
std::vector<std::vector<std::uint8_t>> outputs{execute<float, std::uint8_t, std::uint8_t>(
function, inputs, int_inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_quantize_linear_axis_zero)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/quantize_linear_axis_zero.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{
0.f, 2.f, 3.f, 1000.f, 0.f, 2.f, 3.f, 1000.f, 0.f, 2.f, 3.f, 1000.f}); // x
inputs.emplace_back(std::vector<float>{1.f, 2.f, 4.f}); // y_scale
std::vector<std::vector<std::uint8_t>> int_inputs;
int_inputs.emplace_back(std::vector<std::uint8_t>{0, 0, 0}); // y_zero_point
std::vector<std::vector<std::uint8_t>> expected_output{
// std::vector<std::uint8_t>{0, 2, 3, 255, 0, 1, 2, 255, 0, 1, 1, 250}}; <- bad expected output given HALF_TO_EVEN round mode
std::vector<std::uint8_t>{0, 2, 3, 255, 0, 1, 2, 255, 0, 0, 1, 250}};
std::vector<std::vector<std::uint8_t>> outputs{execute<float, std::uint8_t, std::uint8_t>(
function, inputs, int_inputs, "${BACKEND_NAME}")};
EXPECT_EQ(expected_output.front(), outputs.front());
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_quantize_linear_axis_negative)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/quantize_linear_axis_negative.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{
0.f, 2.f, 3.f, 1000.f, 0.f, 2.f, 3.f, 1000.f, 0.f, 2.f, 3.f, 1000.f}); // x
inputs.emplace_back(std::vector<float>{1.f, 2.f, 4.f}); // y_scale
std::vector<std::vector<std::uint8_t>> int_inputs;
int_inputs.emplace_back(std::vector<std::uint8_t>{0, 0, 0}); // y_zero_point
std::vector<std::vector<std::uint8_t>> expected_output{
// std::vector<std::uint8_t>{0, 2, 3, 255, 0, 1, 2, 255, 0, 1, 1, 250}}; <- bad expected output given HALF_TO_EVEN round mode
std::vector<std::uint8_t>{0, 2, 3, 255, 0, 1, 2, 255, 0, 0, 1, 250}};
std::vector<std::vector<std::uint8_t>> outputs{execute<float, std::uint8_t, std::uint8_t>(
function, inputs, int_inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_dequantize_linear)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/dequant_lin.prototxt"));
std::vector<std::vector<std::uint8_t>> inputs;
inputs.emplace_back(std::vector<std::uint8_t>{19, 210, 21, 10});
Outputs expected_output{std::vector<float>{76.f, 840.f, 84.f, 40.f}};
Outputs outputs{execute<std::uint8_t, float>(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_dequantize_linear_scalar_zero_scale_uint8)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/dequantize_linear_0.prototxt"));
auto x = std::vector<uint8_t>{0, 3, 128, 255};
auto scale = std::vector<float>{2.0f};
auto zero_point = std::vector<uint8_t>{128};
auto backend = ngraph::runtime::Backend::create("${BACKEND_NAME}");
auto params = function->get_parameters();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> input_tensors;
input_tensors.push_back(
backend->create_tensor(params.at(0)->get_element_type(), params.at(0)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(1)->get_element_type(), params.at(1)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(2)->get_element_type(), params.at(2)->get_shape()));
copy_data(input_tensors[0], x);
copy_data(input_tensors[1], scale);
copy_data(input_tensors[2], zero_point);
auto results = function->get_results();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors;
result_tensors.push_back(
backend->create_tensor(results.at(0)->get_element_type(), results.at(0)->get_shape()));
auto handle = backend->compile(function);
handle->call_with_validate(result_tensors, input_tensors);
std::vector<std::vector<float>> outputs;
outputs.push_back(read_vector<float>(result_tensors[0]));
auto expected_output = std::vector<std::vector<float>>{{-256.0f, -250.0f, 0.0f, 254.0f}};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_dequantize_linear_scalar_zero_scale_int8)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/dequantize_linear_1.prototxt"));
auto x = std::vector<int8_t>{-30, -3, 100, 127};
auto scale = std::vector<float>{2.0f};
auto zero_point = std::vector<int8_t>{-10};
auto backend = ngraph::runtime::Backend::create("${BACKEND_NAME}");
auto params = function->get_parameters();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> input_tensors;
input_tensors.push_back(
backend->create_tensor(params.at(0)->get_element_type(), params.at(0)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(1)->get_element_type(), params.at(1)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(2)->get_element_type(), params.at(2)->get_shape()));
copy_data(input_tensors[0], x);
copy_data(input_tensors[1], scale);
copy_data(input_tensors[2], zero_point);
auto results = function->get_results();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors;
result_tensors.push_back(
backend->create_tensor(results.at(0)->get_element_type(), results.at(0)->get_shape()));
auto handle = backend->compile(function);
handle->call_with_validate(result_tensors, input_tensors);
std::vector<std::vector<float>> outputs;
outputs.push_back(read_vector<float>(result_tensors[0]));
auto expected_output = std::vector<std::vector<float>>{{-40.0f, 14.0f, 220.0f, 274.0f}};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_dequantize_linear_1d_zero_scale_uint8)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/dequantize_linear_2.prototxt"));
auto x = std::vector<uint8_t>{0, 1, 2, 3, 0, 1, 2, 3, 0, 10, 20, 30};
auto scale = std::vector<float>{1.0f, 2.0f, 4.0f};
auto zero_point = std::vector<uint8_t>{0, 0, 0};
auto backend = ngraph::runtime::Backend::create("${BACKEND_NAME}");
auto params = function->get_parameters();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> input_tensors;
input_tensors.push_back(
backend->create_tensor(params.at(0)->get_element_type(), params.at(0)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(1)->get_element_type(), params.at(1)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(2)->get_element_type(), params.at(2)->get_shape()));
copy_data(input_tensors[0], x);
copy_data(input_tensors[1], scale);
copy_data(input_tensors[2], zero_point);
auto results = function->get_results();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors;
result_tensors.push_back(
backend->create_tensor(results.at(0)->get_element_type(), results.at(0)->get_shape()));
auto handle = backend->compile(function);
handle->call_with_validate(result_tensors, input_tensors);
std::vector<std::vector<float>> outputs;
outputs.push_back(read_vector<float>(result_tensors[0]));
auto expected_output = std::vector<std::vector<float>>{
{0.0f, 1.0f, 2.0f, 3.0f, 0.0f, 2.0f, 4.0f, 6.0f, 0.0f, 40.0f, 80.0f, 120.0f}};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_dequantize_linear_1d_zero_scale_int8)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/dequantize_linear_3.prototxt"));
auto x = std::vector<int8_t>{0, 1, 2, 3, 0, 2, 4, 6, 0, 10, 20, 30};
auto scale = std::vector<float>{1.0f, 2.0f, 4.0f, 8.0f};
auto zero_point = std::vector<int8_t>{0, -10, -20, -30};
auto backend = ngraph::runtime::Backend::create("${BACKEND_NAME}");
auto params = function->get_parameters();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> input_tensors;
input_tensors.push_back(
backend->create_tensor(params.at(0)->get_element_type(), params.at(0)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(1)->get_element_type(), params.at(1)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(2)->get_element_type(), params.at(2)->get_shape()));
copy_data(input_tensors[0], x);
copy_data(input_tensors[1], scale);
copy_data(input_tensors[2], zero_point);
auto results = function->get_results();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors;
result_tensors.push_back(
backend->create_tensor(results.at(0)->get_element_type(), results.at(0)->get_shape()));
auto handle = backend->compile(function);
handle->call_with_validate(result_tensors, input_tensors);
std::vector<std::vector<float>> outputs;
outputs.push_back(read_vector<float>(result_tensors[0]));
auto expected_output = std::vector<std::vector<float>>{
{0.0f, 22.0f, 88.0f, 264.0f, 0.0f, 24.0f, 96.0f, 288.0f, 0.0f, 40.0f, 160.0f, 480.0f}};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_dequantize_linear_1d_zero_scale_int8_4d)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/dequantize_linear_4.prototxt"));
auto x = std::vector<int8_t>{7, 9, 10, 10, 5, 8, 9, 1, 8, 6, 7, 9, 10, 0, 7, 10,
8, 2, 6, 0, 5, 9, 8, 1, 2, 7, 5, 3, 2, 4, 1, 3,
8, 7, 4, 8, 10, 1, 5, 5, 7, 7, 0, 2, 4, 4, 0, 5};
auto scale = std::vector<float>{1.0f, 10.0f, 7.0f};
auto zero_point = std::vector<int8_t>{10, 2, 1};
auto backend = ngraph::runtime::Backend::create("${BACKEND_NAME}");
auto params = function->get_parameters();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> input_tensors;
input_tensors.push_back(
backend->create_tensor(params.at(0)->get_element_type(), params.at(0)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(1)->get_element_type(), params.at(1)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(2)->get_element_type(), params.at(2)->get_shape()));
copy_data(input_tensors[0], x);
copy_data(input_tensors[1], scale);
copy_data(input_tensors[2], zero_point);
auto results = function->get_results();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors;
result_tensors.push_back(
backend->create_tensor(results.at(0)->get_element_type(), results.at(0)->get_shape()));
auto handle = backend->compile(function);
handle->call_with_validate(result_tensors, input_tensors);
std::vector<std::vector<float>> outputs;
outputs.push_back(read_vector<float>(result_tensors[0]));
auto expected_output = std::vector<std::vector<float>>{
{-3.0f, -1.0f, 0.0f, 0.0f, -5.0f, -2.0f, -1.0f, -9.0f, 60.0f, 40.0f, 50.0f, 70.0f,
80.0f, -20.0f, 50.0f, 80.0f, 49.0f, 7.0f, 35.0f, -7.0f, 28.0f, 56.0f, 49.0f, 0.0f,
-8.0f, -3.0f, -5.0f, -7.0f, -8.0f, -6.0f, -9.0f, -7.0f, 60.0f, 50.0f, 20.0f, 60.0f,
80.0f, -10.0f, 30.0f, 30.0f, 42.0f, 42.0f, -7.0f, 7.0f, 21.0f, 21.0f, -7.0f, 28.0f}};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_dequantize_linear_1d_zero_scale_uint8_negative_axis)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/dequantize_linear_5.prototxt"));
auto x = std::vector<uint8_t>{0, 1, 2, 3, 0, 1, 2, 3, 0, 10, 20, 30};
auto scale = std::vector<float>{1.0f, 2.0f, 4.0f};
auto zero_point = std::vector<uint8_t>{0, 0, 0};
auto backend = ngraph::runtime::Backend::create("${BACKEND_NAME}");
auto params = function->get_parameters();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> input_tensors;
input_tensors.push_back(
backend->create_tensor(params.at(0)->get_element_type(), params.at(0)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(1)->get_element_type(), params.at(1)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(2)->get_element_type(), params.at(2)->get_shape()));
copy_data(input_tensors[0], x);
copy_data(input_tensors[1], scale);
copy_data(input_tensors[2], zero_point);
auto results = function->get_results();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors;
result_tensors.push_back(
backend->create_tensor(results.at(0)->get_element_type(), results.at(0)->get_shape()));
auto handle = backend->compile(function);
handle->call_with_validate(result_tensors, input_tensors);
std::vector<std::vector<float>> outputs;
outputs.push_back(read_vector<float>(result_tensors[0]));
auto expected_output = std::vector<std::vector<float>>{
{0.0f, 1.0f, 2.0f, 3.0f, 0.0f, 2.0f, 4.0f, 6.0f, 0.0f, 40.0f, 80.0f, 120.0f}};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_quant_conv_linear)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/quant_conv_lin.prototxt"));
std::vector<std::vector<std::uint8_t>> inputs;
inputs.emplace_back(std::vector<std::uint8_t>{
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81});
std::vector<std::vector<std::int8_t>> expected_output{std::vector<std::int8_t>{
2, 3, 3, 3, 4, 4, 4, 5, 2, 4, 6, 7, 8, 8, 9, 9, 10, 3, 8, 11, 12,
13, 13, 14, 14, 15, 5, 11, 16, 17, 18, 18, 19, 19, 20, 7, 14, 22, 22, 23, 23, 24,
24, 25, 8, 18, 27, 27, 28, 28, 29, 29, 30, 10, 21, 32, 32, 33, 33, 34, 34, 35, 12,
24, 37, 37, 38, 38, 39, 40, 40, 13, 17, 26, 27, 27, 27, 28, 28, 28, 9}};
std::vector<std::vector<std::int8_t>> outputs{
execute<std::uint8_t, std::int8_t>(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_quant_conv_linear_2d)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/qlinear_conv_2d.prototxt"));
auto x =
read_binary_file<uint8_t>(file_util::path_join(TEST_FILES, "onnx/qlinearconv2d/x.bin"));
auto x_scale =
read_binary_file<float>(file_util::path_join(TEST_FILES, "onnx/qlinearconv2d/x_scale.bin"));
auto x_zero_point = read_binary_file<uint8_t>(
file_util::path_join(TEST_FILES, "onnx/qlinearconv2d/x_zero_point.bin"));
auto w =
read_binary_file<uint8_t>(file_util::path_join(TEST_FILES, "onnx/qlinearconv2d/w.bin"));
auto w_scale =
read_binary_file<float>(file_util::path_join(TEST_FILES, "onnx/qlinearconv2d/w_scale.bin"));
auto w_zero_point = read_binary_file<uint8_t>(
file_util::path_join(TEST_FILES, "onnx/qlinearconv2d/w_zero_point.bin"));
auto y_scale =
read_binary_file<float>(file_util::path_join(TEST_FILES, "onnx/qlinearconv2d/y_scale.bin"));
auto y_zero_point = read_binary_file<uint8_t>(
file_util::path_join(TEST_FILES, "onnx/qlinearconv2d/y_zero_point.bin"));
auto backend = ngraph::runtime::Backend::create("${BACKEND_NAME}");
auto params = function->get_parameters();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> input_tensors;
input_tensors.push_back(
backend->create_tensor(params.at(0)->get_element_type(), params.at(0)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(1)->get_element_type(), params.at(1)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(2)->get_element_type(), params.at(2)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(3)->get_element_type(), params.at(3)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(4)->get_element_type(), params.at(4)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(5)->get_element_type(), params.at(5)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(6)->get_element_type(), params.at(6)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(7)->get_element_type(), params.at(7)->get_shape()));
copy_data(input_tensors[0], x);
copy_data(input_tensors[1], x_scale);
copy_data(input_tensors[2], x_zero_point);
copy_data(input_tensors[3], w);
copy_data(input_tensors[4], w_scale);
copy_data(input_tensors[5], w_zero_point);
copy_data(input_tensors[6], y_scale);
copy_data(input_tensors[7], y_zero_point);
auto results = function->get_results();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors;
result_tensors.push_back(
backend->create_tensor(results.at(0)->get_element_type(), results.at(0)->get_shape()));
auto handle = backend->compile(function);
handle->call_with_validate(result_tensors, input_tensors);
std::vector<std::vector<uint8_t>> outputs;
outputs.push_back(read_vector<uint8_t>(result_tensors[0]));
std::vector<std::vector<uint8_t>> expected_output;
expected_output.push_back(
read_binary_file<uint8_t>(file_util::path_join(TEST_FILES, "onnx/qlinearconv2d/y.bin")));
EXPECT_EQ(expected_output.front(), outputs.front());
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_quant_conv_linear_3d)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/qlinear_conv_3d.prototxt"));
auto x =
read_binary_file<uint8_t>(file_util::path_join(TEST_FILES, "onnx/qlinearconv3d/x.bin"));
auto x_scale =
read_binary_file<float>(file_util::path_join(TEST_FILES, "onnx/qlinearconv3d/x_scale.bin"));
auto x_zero_point = read_binary_file<uint8_t>(
file_util::path_join(TEST_FILES, "onnx/qlinearconv3d/x_zero_point.bin"));
auto w =
read_binary_file<uint8_t>(file_util::path_join(TEST_FILES, "onnx/qlinearconv3d/w.bin"));
auto w_scale =
read_binary_file<float>(file_util::path_join(TEST_FILES, "onnx/qlinearconv3d/w_scale.bin"));
auto w_zero_point = read_binary_file<uint8_t>(
file_util::path_join(TEST_FILES, "onnx/qlinearconv3d/w_zero_point.bin"));
auto y_scale =
read_binary_file<float>(file_util::path_join(TEST_FILES, "onnx/qlinearconv3d/y_scale.bin"));
auto y_zero_point = read_binary_file<uint8_t>(
file_util::path_join(TEST_FILES, "onnx/qlinearconv3d/y_zero_point.bin"));
auto backend = ngraph::runtime::Backend::create("${BACKEND_NAME}");
auto params = function->get_parameters();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> input_tensors;
input_tensors.push_back(
backend->create_tensor(params.at(0)->get_element_type(), params.at(0)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(1)->get_element_type(), params.at(1)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(2)->get_element_type(), params.at(2)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(3)->get_element_type(), params.at(3)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(4)->get_element_type(), params.at(4)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(5)->get_element_type(), params.at(5)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(6)->get_element_type(), params.at(6)->get_shape()));
input_tensors.push_back(
backend->create_tensor(params.at(7)->get_element_type(), params.at(7)->get_shape()));
copy_data(input_tensors[0], x);
copy_data(input_tensors[1], x_scale);
copy_data(input_tensors[2], x_zero_point);
copy_data(input_tensors[3], w);
copy_data(input_tensors[4], w_scale);
copy_data(input_tensors[5], w_zero_point);
copy_data(input_tensors[6], y_scale);
copy_data(input_tensors[7], y_zero_point);
auto results = function->get_results();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors;
result_tensors.push_back(
backend->create_tensor(results.at(0)->get_element_type(), results.at(0)->get_shape()));
auto handle = backend->compile(function);
handle->call_with_validate(result_tensors, input_tensors);
std::vector<std::vector<uint8_t>> outputs;
outputs.push_back(read_vector<uint8_t>(result_tensors[0]));
std::vector<std::vector<uint8_t>> expected_output;
expected_output.push_back(
read_binary_file<uint8_t>(file_util::path_join(TEST_FILES, "onnx/qlinearconv3d/y.bin")));
EXPECT_EQ(expected_output.front(), outputs.front());
}
test/onnx/onnx_import_reshape.in.cpp 0 → 100644
View file @ 57d637bc
//*****************************************************************************
// Copyright 2017-2019 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//*****************************************************************************
#include <algorithm>
#include <cmath>
#include <cstdint>
#include <fstream>
#include <iterator>
#include <limits>
#include <sstream>
#include <stdexcept>
#include <vector>
#include "gtest/gtest.h"
#include "ngraph/frontend/onnx_import/onnx.hpp"
#include "ngraph/ngraph.hpp"
#include "util/all_close.hpp"
#include "util/all_close_f.hpp"
#include "util/ndarray.hpp"
#include "util/test_control.hpp"
#include "util/test_tools.hpp"
using namespace ngraph;
static std::string s_manifest = "${MANIFEST}";
using Inputs = std::vector<std::vector<float>>;
using Outputs = std::vector<std::vector<float>>;
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_reshape_reduced_dims)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_reduced_dims.prototxt"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (2, 12)
Outputs expected_outputs{
test::NDArray<float, 2>({{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11},
{12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_reshape_reordered_dims)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_reordered_dims.prototxt"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (4, 2, 3)
Outputs expected_outputs{test::NDArray<float, 3>({{{0, 1, 2}, {3, 4, 5}},
{{6, 7, 8}, {9, 10, 11}},
{{12, 13, 14}, {15, 16, 17}},
{{18, 19, 20}, {21, 22, 23}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_reshape_extended_dims)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_extended_dims.prototxt"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (3, 2, 2, 2)
Outputs expected_outputs{test::NDArray<float, 4>({{{{0, 1}, {2, 3}}, {{4, 5}, {6, 7}}},
{{{8, 9}, {10, 11}}, {{12, 13}, {14, 15}}},
{{{16, 17}, {18, 19}}, {{20, 21}, {22, 23}}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_reshape_single_dim)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_single_dim.prototxt"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (24, )
Outputs expected_outputs{
test::NDArray<float, 1>(
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_reshape_negative_dim)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_negative_dim.prototxt"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (6, 2, 2)
Outputs expected_outputs{test::NDArray<float, 3>({{{0, 1}, {2, 3}},
{{4, 5}, {6, 7}},
{{8, 9}, {10, 11}},
{{12, 13}, {14, 15}},
{{16, 17}, {18, 19}},
{{20, 21}, {22, 23}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_reshape_negative_with_zero_dim)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_negative_with_zero_dims.prototxt"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (2, 6, 2)
Outputs expected_outputs{
test::NDArray<float, 3>({{{0, 1}, {2, 3}, {4, 5}, {6, 7}, {8, 9}, {10, 11}},
{{12, 13}, {14, 15}, {16, 17}, {18, 19}, {20, 21}, {22, 23}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_reshape_output_shape_as_input)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_output_shape_as_input.prototxt"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (2, 6, 2)
Outputs expected_outputs{
test::NDArray<float, 3>({{{0, 1}, {2, 3}, {4, 5}, {6, 7}, {8, 9}, {10, 11}},
{{12, 13}, {14, 15}, {16, 17}, {18, 19}, {20, 21}, {22, 23}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_depth_to_space)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/depth_to_space.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{
0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f});
Outputs expected_output{std::vector<float>{
0.f, 4.f, 1.f, 5.f, 8.f, 12.f, 9.f, 13.f, 2.f, 6.f, 3.f, 7.f, 10.f, 14.f, 11.f, 15.f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_depth_to_space_chw)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/depth_to_space_chw.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{
0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f});
Outputs expected_output{std::vector<float>{
0.f, 4.f, 1.f, 5.f, 8.f, 12.f, 9.f, 13.f, 2.f, 6.f, 3.f, 7.f, 10.f, 14.f, 11.f, 15.f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_depth_to_space_bad_blocksize)
{
// This model fails to import since the depth channel length must be a multiple of the
// `blocksize` attribute value.
EXPECT_THROW(onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/depth_to_space_bad_blocksize.prototxt")),
std::runtime_error);
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_depth_to_space_no_blocksize)
{
// This model fails to import since it lacks of required parameter `blocksize`.
EXPECT_THROW(onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/depth_to_space_no_blocksize.prototxt")),
std::runtime_error);
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_space_to_depth)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/space_to_depth.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f,
8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f,
16.f, 17.f, 18.f, 19.f, 20.f, 21.f, 22.f, 23.f,
24.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 31.f});
Outputs expected_output{std::vector<float>{
0.f, 2.f, 8.f, 10.f, 16.f, 18.f, 24.f, 26.f, 1.f, 3.f, 9.f, 11.f, 17.f, 19.f, 25.f, 27.f,
4.f, 6.f, 12.f, 14.f, 20.f, 22.f, 28.f, 30.f, 5.f, 7.f, 13.f, 15.f, 21.f, 23.f, 29.f, 31.f,
}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_space_to_depth_chw)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/space_to_depth_chw.prototxt"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f,
8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f,
16.f, 17.f, 18.f, 19.f, 20.f, 21.f, 22.f, 23.f,
24.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 31.f});
Outputs expected_output{std::vector<float>{
0.f, 2.f, 8.f, 10.f, 16.f, 18.f, 24.f, 26.f, 1.f, 3.f, 9.f, 11.f, 17.f, 19.f, 25.f, 27.f,
4.f, 6.f, 12.f, 14.f, 20.f, 22.f, 28.f, 30.f, 5.f, 7.f, 13.f, 15.f, 21.f, 23.f, 29.f, 31.f,
}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_space_to_depth_bad_blocksize)
{
// This model fails to import since the depth channel length must be a multiple of the
// `blocksize` attribute value.
EXPECT_THROW(onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/space_to_depth_bad_blocksize.prototxt")),
std::runtime_error);
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_space_to_depth_no_blocksize)
{
// This model fails to import since it lacks of required `blocksize` attribute.
EXPECT_THROW(onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/space_to_depth_no_blocksize.prototxt")),
std::runtime_error);
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_squeeze)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/squeeze_duplicate_axes.prototxt"));
// {1, 4, 1, 1, 2}
Inputs inputs{test::NDArray<float, 5>(
{{{{{1.0f, 2.0f}}}, {{{3.0f, 4.0f}}}, {{{5.0f, 6.0f}}}, {{{7.0f, 8.0f}}}}})
.get_vector()};
// {4, 2}
Outputs expected_output{
test::NDArray<float, 2>({{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}, {7.0f, 8.0f}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_unsqueeze)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/unsqueeze.prototxt"));
Inputs inputs;
inputs.emplace_back(test::NDArray<float, 3>(
{{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}},
{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}},
{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}}})
.get_vector());
Outputs expected_output{
test::NDArray<float, 4>(
{{{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}},
{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}},
{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_concat)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/concat.prototxt"));
Inputs inputs;
inputs.emplace_back(test::NDArray<float, 1>({1, 2}).get_vector());
inputs.emplace_back(test::NDArray<float, 1>({3, 4}).get_vector());
Outputs expected_outputs{test::NDArray<float, 1>({1, 2, 3, 4}).get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_flatten)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/flatten.prototxt"));
Inputs inputs;
inputs.emplace_back(
test::NDArray<float, 4>({{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}}).get_vector());
Outputs expected_outputs{test::NDArray<float, 3>({{{1, 2, 3, 4}, {5, 6, 7, 8}}}).get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_split_equal_parts_default)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/split_equal_parts_default.prototxt"));
Inputs inputs{{1, 2, 3, 4, 5, 6}};
Outputs expected_outputs{{1, 2}, {3, 4}, {5, 6}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_EQ(outputs.size(), expected_outputs.size());
for (std::size_t i = 0; i < expected_outputs.size(); ++i)
{
EXPECT_EQ(outputs[i].size(), expected_outputs[i].size());
EXPECT_TRUE(test::all_close_f(outputs[i], expected_outputs[i]));
}
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_split_equal_parts_2d)
{
// Split into 2 equal parts along axis=1
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/split_equal_parts_2d.prototxt"));
Inputs inputs{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}};
Outputs expected_outputs{{0, 1, 2, 6, 7, 8}, {3, 4, 5, 9, 10, 11}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_EQ(outputs.size(), expected_outputs.size());
for (std::size_t i = 0; i < expected_outputs.size(); ++i)
{
EXPECT_EQ(outputs[i].size(), expected_outputs[i].size());
EXPECT_TRUE(test::all_close_f(outputs[i], expected_outputs[i]));
}
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_split_variable_parts_2d)
{
// Split into variable parts {2, 4} along axis=1
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/split_variable_parts_2d.prototxt"));
Inputs inputs{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}};
Outputs expected_outputs{{0, 1, 6, 7}, {2, 3, 4, 5, 8, 9, 10, 11}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_EQ(outputs.size(), expected_outputs.size());
for (std::size_t i = 0; i < expected_outputs.size(); ++i)
{
EXPECT_EQ(outputs[i].size(), expected_outputs[i].size());
EXPECT_TRUE(test::all_close_f(outputs[i], expected_outputs[i]));
}
}
test/onnx/onnx_import_rnn.in.cpp 0 → 100644
View file @ 57d637bc
//*****************************************************************************
// Copyright 2017-2019 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//*****************************************************************************
#include <algorithm>
#include <cmath>
#include <cstdint>
#include <fstream>
#include <iterator>
#include <limits>
#include <sstream>
#include <stdexcept>
#include <vector>
#include "gtest/gtest.h"
#include "ngraph/frontend/onnx_import/onnx.hpp"
#include "ngraph/ngraph.hpp"
#include "util/all_close.hpp"
#include "util/all_close_f.hpp"
#include "util/ndarray.hpp"
#include "util/test_control.hpp"
#include "util/test_tools.hpp"
using namespace ngraph;
static std::string s_manifest = "${MANIFEST}";
using Inputs = std::vector<std::vector<float>>;
using Outputs = std::vector<std::vector<float>>;
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_lstm_fwd_with_clip)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/lstm_fwd_with_clip.prototxt"));
Inputs inputs{};
// X
inputs.emplace_back(std::vector<float>{-0.455351, -0.276391, -0.185934, -0.269585});
// W
inputs.emplace_back(std::vector<float>{-0.494659f,
0.0453352f,
-0.487793f,
0.417264f,
-0.0175329f,
0.489074f,
-0.446013f,
0.414029f,
-0.0091708f,
-0.255364f,
-0.106952f,
-0.266717f,
-0.0888852f,
-0.428709f,
-0.283349f,
0.208792f});
// R
inputs.emplace_back(std::vector<float>{0.146626f,
-0.0620289f,
-0.0815302f,
0.100482f,
-0.219535f,
-0.306635f,
-0.28515f,
-0.314112f,
-0.228172f,
0.405972f,
0.31576f,
0.281487f,
-0.394864f,
0.42111f,
-0.386624f,
-0.390225f});
// B
inputs.emplace_back(std::vector<float>{0.381619f,
0.0323954f,
-0.14449f,
0.420804f,
-0.258721f,
0.45056f,
-0.250755f,
0.0967895f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f,
0.0f});
// P
inputs.emplace_back(std::vector<float>{0.2345f, 0.5235f, 0.4378f, 0.3475f, 0.8927f, 0.3456f});
Outputs expected_output{};
// Y_data
expected_output.emplace_back(
std::vector<float>{-0.02280854f, 0.02744377f, -0.03516197f, 0.03875681f});
// Y_h_data
expected_output.emplace_back(std::vector<float>{-0.03516197f, 0.03875681f});
// Y_c_data
expected_output.emplace_back(std::vector<float>{-0.07415761f, 0.07395997f});
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(outputs.size() == expected_output.size());
for (std::size_t i{0}; i < expected_output.size(); ++i)
{
// We have to enlarge tolerance bits to 3 - it's only one bit more than default value.
// The discrepancies may occur at most on 7th decimal position.
EXPECT_TRUE(test::all_close_f(expected_output.at(i), outputs.at(i), 3));
}
}
NGRAPH_TEST(onnx_${BACKEND_NAME}, model_lstm_fwd_mixed_seq)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/lstm_fwd_mixed_seq.prototxt"));
int hidden_size{3};
int parameters_cout{5};
// X
std::vector<float> in_x{1.f, 2.f, 10.f, 11.f};
// W
std::vector<float> in_w{0.1f, 0.2f, 0.3f, 0.4f, 1.f, 2.f, 3.f, 4.f, 10.f, 11.f, 12.f, 13.f};
// R
std::vector<float> in_r(4 * hidden_size * hidden_size, 0.1f);
// B
std::vector<float> in_b(8 * hidden_size, 0.0f);
std::vector<int> in_seq_lengths{1, 2};
std::vector<float> out_y_data{0.28828835f,
0.36581863f,
0.45679406f,
0.34526032f,
0.47220859f,
0.55850911f,
0.f,
0.f,
0.f,
0.85882828f,
0.90703777f,
0.92382453f};
std::vector<float> out_y_h_data{
0.28828835f, 0.36581863f, 0.45679406f, 0.85882828f, 0.90703777f, 0.92382453f};
std::vector<float> out_y_c_data{
0.52497941f, 0.54983425f, 0.5744428f, 1.3249796f, 1.51063104f, 1.61451544f};
Outputs expected_output;
expected_output.emplace_back(out_y_data);
expected_output.emplace_back(out_y_h_data);
expected_output.emplace_back(out_y_c_data);
auto backend = ngraph::runtime::Backend::create("${BACKEND_NAME}");
auto parameters = function->get_parameters();
EXPECT_TRUE(parameters.size() == parameters_cout);
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> arg_tensors;
auto add_tensor = [&arg_tensors, &backend](const std::vector<float>& v,
const std::shared_ptr<ngraph::op::Parameter>& p) {
auto t = backend->create_tensor(p->get_element_type(), p->get_shape());
copy_data(t, v);
arg_tensors.push_back(t);
};
add_tensor(in_x, parameters.at(0));
add_tensor(in_w, parameters.at(1));
add_tensor(in_r, parameters.at(2));
add_tensor(in_b, parameters.at(3));
auto t_in_seq_lengths =
backend->create_tensor(parameters.at(4)->get_element_type(), parameters.at(4)->get_shape());
copy_data(t_in_seq_lengths, in_seq_lengths);
arg_tensors.push_back(t_in_seq_lengths);
auto results = function->get_results();
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors(results.size());
for (std::size_t i{0}; i < results.size(); ++i)
{
result_tensors.at(i) =
backend->create_tensor(results.at(i)->get_element_type(), results.at(i)->get_shape());
}
auto handle = backend->compile(function);
handle->call_with_validate(result_tensors, arg_tensors);
Outputs outputs;
for (auto rt : result_tensors)
{
outputs.push_back(read_vector<float>(rt));
}
EXPECT_TRUE(outputs.size() == expected_output.size());
for (std::size_t i{0}; i < expected_output.size(); ++i)
{
// We have to enlarge tolerance bits to 3 - it's only one bit more than default value.
// The discrepancies may occur at most on 7th decimal position.
EXPECT_TRUE(test::all_close_f(expected_output.at(i), outputs.at(i), 3));
}
}
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