diff options
Diffstat (limited to 'eval/src/tests/tensor/onnx_wrapper/onnx_wrapper_test.cpp')
| -rw-r--r-- | eval/src/tests/tensor/onnx_wrapper/onnx_wrapper_test.cpp | 76 |
1 files changed, 56 insertions, 20 deletions
diff --git a/eval/src/tests/tensor/onnx_wrapper/onnx_wrapper_test.cpp b/eval/src/tests/tensor/onnx_wrapper/onnx_wrapper_test.cpp index db2415e9969..23c41167266 100644 --- a/eval/src/tests/tensor/onnx_wrapper/onnx_wrapper_test.cpp +++ b/eval/src/tests/tensor/onnx_wrapper/onnx_wrapper_test.cpp @@ -11,6 +11,7 @@ using namespace vespalib::tensor; using vespalib::make_string_short::fmt; using TensorInfo = Onnx::TensorInfo; +using ElementType = Onnx::ElementType; using DZ = Onnx::DimSize; std::string get_source_dir() { @@ -20,6 +21,7 @@ std::string get_source_dir() { std::string source_dir = get_source_dir(); std::string simple_model = source_dir + "/simple.onnx"; std::string dynamic_model = source_dir + "/dynamic.onnx"; +std::string int_types_model = source_dir + "/int_types.onnx"; void dump_info(const char *ctx, const std::vector<TensorInfo> &info) { fprintf(stderr, "%s:\n", ctx); @@ -28,24 +30,12 @@ void dump_info(const char *ctx, const std::vector<TensorInfo> &info) { } } -TEST(WirePlannerTest, element_types_must_match) { - Onnx::WirePlanner planner; - ValueType type1 = ValueType::from_spec("tensor<float>(a[5])"); - ValueType type2 = ValueType::from_spec("tensor<double>(a[5])"); - TensorInfo info1 = TensorInfo{"info", {DZ(5)}, TensorInfo::ElementType::FLOAT}; - TensorInfo info2 = TensorInfo{"info", {DZ(5)}, TensorInfo::ElementType::DOUBLE}; - EXPECT_TRUE(planner.bind_input_type(type1, info1)); - EXPECT_FALSE(planner.bind_input_type(type2, info1)); - EXPECT_FALSE(planner.bind_input_type(type1, info2)); - EXPECT_TRUE(planner.bind_input_type(type2, info2)); -} - TEST(WirePlannerTest, known_dimension_sizes_must_match) { Onnx::WirePlanner planner; ValueType type1 = ValueType::from_spec("tensor<float>(a[5],b[10])"); ValueType type2 = ValueType::from_spec("tensor<float>(a[10],b[5])"); ValueType type3 = ValueType::from_spec("tensor<float>(a[5],b[5])"); - TensorInfo info = TensorInfo{"info", {DZ(5),DZ(5)}, TensorInfo::ElementType::FLOAT}; + TensorInfo info = TensorInfo{"info", {DZ(5),DZ(5)}, ElementType::FLOAT}; EXPECT_FALSE(planner.bind_input_type(type1, info)); EXPECT_FALSE(planner.bind_input_type(type2, info)); EXPECT_TRUE(planner.bind_input_type(type3, info)); @@ -55,7 +45,7 @@ TEST(WirePlannerTest, symbolic_dimension_sizes_must_match) { Onnx::WirePlanner planner; ValueType type1 = ValueType::from_spec("tensor<float>(a[5])"); ValueType type2 = ValueType::from_spec("tensor<float>(a[10])"); - TensorInfo info = TensorInfo{"info", {DZ("dim")}, TensorInfo::ElementType::FLOAT}; + TensorInfo info = TensorInfo{"info", {DZ("dim")}, ElementType::FLOAT}; EXPECT_TRUE(planner.bind_input_type(type1, info)); // binds 'dim' to 5 EXPECT_FALSE(planner.bind_input_type(type2, info)); EXPECT_TRUE(planner.bind_input_type(type1, info)); @@ -65,7 +55,7 @@ TEST(WirePlannerTest, unknown_dimension_sizes_match_anything) { Onnx::WirePlanner planner; ValueType type1 = ValueType::from_spec("tensor<float>(a[5])"); ValueType type2 = ValueType::from_spec("tensor<float>(a[10])"); - TensorInfo info = TensorInfo{"info", {DZ()}, TensorInfo::ElementType::FLOAT}; + TensorInfo info = TensorInfo{"info", {DZ()}, ElementType::FLOAT}; EXPECT_TRUE(planner.bind_input_type(type1, info)); EXPECT_TRUE(planner.bind_input_type(type2, info)); } @@ -73,9 +63,9 @@ TEST(WirePlannerTest, unknown_dimension_sizes_match_anything) { TEST(WirePlannerTest, all_output_dimensions_must_be_bound) { Onnx::WirePlanner planner; ValueType type = ValueType::from_spec("tensor<float>(a[5],b[10])"); - TensorInfo info1 = TensorInfo{"info", {DZ()}, TensorInfo::ElementType::FLOAT}; - TensorInfo info2 = TensorInfo{"info", {DZ("dim")}, TensorInfo::ElementType::FLOAT}; - TensorInfo info3 = TensorInfo{"info", {DZ("dim"),DZ()}, TensorInfo::ElementType::FLOAT}; + TensorInfo info1 = TensorInfo{"info", {DZ()}, ElementType::FLOAT}; + TensorInfo info2 = TensorInfo{"info", {DZ("dim")}, ElementType::FLOAT}; + TensorInfo info3 = TensorInfo{"info", {DZ("dim"),DZ()}, ElementType::FLOAT}; EXPECT_TRUE(planner.make_output_type(info1).is_error()); EXPECT_TRUE(planner.make_output_type(info2).is_error()); EXPECT_TRUE(planner.make_output_type(info3).is_error()); @@ -90,7 +80,7 @@ TEST(WirePlannerTest, dimensions_resolve_left_to_right) { ValueType type1 = ValueType::from_spec("tensor<float>(a[5],b[10])"); ValueType type2 = ValueType::from_spec("tensor<float>(a[10],b[10])"); ValueType type3 = ValueType::from_spec("tensor<float>(a[5],b[5])"); - TensorInfo info = TensorInfo{"info", {DZ("dim"),DZ("dim")}, TensorInfo::ElementType::FLOAT}; + TensorInfo info = TensorInfo{"info", {DZ("dim"),DZ("dim")}, ElementType::FLOAT}; EXPECT_FALSE(planner.bind_input_type(type1, info)); // binds 'dim' to 5, then fails (5 != 10) EXPECT_FALSE(planner.bind_input_type(type2, info)); EXPECT_TRUE(planner.bind_input_type(type3, info)); @@ -180,7 +170,7 @@ TEST(OnnxTest, simple_onnx_model_can_be_evaluated) DenseTensorView new_bias(bias_type, TypedCells(new_bias_values)); ctx.bind_param(2, new_bias); ctx.eval(); - EXPECT_EQ(static_cast<const DenseTensorView&>(output).cellsRef().get(0), 80.0); + EXPECT_EQ(static_cast<const DenseTensorView&>(output).cellsRef().get(0), 80.0); //------------------------------------------------------------------------- } @@ -230,4 +220,50 @@ TEST(OnnxTest, dynamic_onnx_model_can_be_evaluated) //------------------------------------------------------------------------- } +TEST(OnnxTest, int_types_onnx_model_can_be_evaluated) +{ + Onnx model(int_types_model, Onnx::Optimize::ENABLE); + Onnx::WirePlanner planner; + + ValueType query_type = ValueType::from_spec("tensor<float>(a[1],b[4])"); + std::vector<float> query_values({1.0, 2.0, 3.0, 4.0}); + DenseTensorView query(query_type, TypedCells(query_values)); + EXPECT_TRUE(planner.bind_input_type(query_type, model.inputs()[0])); + + ValueType attribute_type = ValueType::from_spec("tensor<double>(a[4],b[1])"); + std::vector<double> attribute_values({5.0, 6.0, 7.0, 8.0}); + DenseTensorView attribute(attribute_type, TypedCells(attribute_values)); + EXPECT_TRUE(planner.bind_input_type(attribute_type, model.inputs()[1])); + + ValueType bias_type = ValueType::from_spec("tensor<double>(a[1],b[1])"); + std::vector<double> bias_values({9.0}); + DenseTensorView bias(bias_type, TypedCells(bias_values)); + EXPECT_TRUE(planner.bind_input_type(bias_type, model.inputs()[2])); + + EXPECT_EQ(planner.make_output_type(model.outputs()[0]), + ValueType::from_spec("tensor<double>(d0[1],d1[1])")); + + Onnx::WireInfo wire_info = planner.get_wire_info(model); + Onnx::EvalContext ctx(model, wire_info); + + const Value &output = ctx.get_result(0); + EXPECT_EQ(output.type(), ValueType::from_spec("tensor<double>(d0[1],d1[1])")); + //------------------------------------------------------------------------- + ctx.bind_param(0, query); + ctx.bind_param(1, attribute); + ctx.bind_param(2, bias); + ctx.eval(); + auto cells = static_cast<const DenseTensorView&>(output).cellsRef(); + EXPECT_EQ(cells.type, ValueType::CellType::DOUBLE); + EXPECT_EQ(cells.size, 1); + EXPECT_EQ(cells.get(0), 79.0); + //------------------------------------------------------------------------- + std::vector<double> new_bias_values({10.0}); + DenseTensorView new_bias(bias_type, TypedCells(new_bias_values)); + ctx.bind_param(2, new_bias); + ctx.eval(); + EXPECT_EQ(static_cast<const DenseTensorView&>(output).cellsRef().get(0), 80.0); + //------------------------------------------------------------------------- +} + GTEST_MAIN_RUN_ALL_TESTS() |