Loading neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp +6 −3 Original line number Diff line number Diff line Loading @@ -66,7 +66,8 @@ void copy_back(MixedTyped* dst, const std::vector<RequestArgument>& ra, char* sr // Top level driver for models and examples generated by test_generator.py // Test driver for those generated from ml/nn/runtime/test/spec void EvaluatePreparedModel(sp<IPreparedModel>& preparedModel, std::function<bool(int)> is_ignored, const std::vector<MixedTypedExampleType>& examples) { const std::vector<MixedTypedExampleType>& examples, float fpRange = 1e-5f) { const uint32_t INPUT = 0; const uint32_t OUTPUT = 1; Loading Loading @@ -174,7 +175,7 @@ void EvaluatePreparedModel(sp<IPreparedModel>& preparedModel, std::function<bool MixedTyped filtered_test = filter(test, is_ignored); // We want "close-enough" results for float compare(filtered_golden, filtered_test); compare(filtered_golden, filtered_test, fpRange); } } Loading Loading @@ -274,7 +275,9 @@ void Execute(sp<V1_1::IDevice>& device, std::function<V1_1::Model(void)> create_ } ASSERT_NE(nullptr, preparedModel.get()); EvaluatePreparedModel(preparedModel, is_ignored, examples); // If in relaxed mode, set the error range to be 5ULP of FP16. float fpRange = !model.relaxComputationFloat32toFloat16 ? 1e-5f : 5.0f * 0.0009765625f; EvaluatePreparedModel(preparedModel, is_ignored, examples, fpRange); } } // namespace generated_tests Loading Loading
neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp +6 −3 Original line number Diff line number Diff line Loading @@ -66,7 +66,8 @@ void copy_back(MixedTyped* dst, const std::vector<RequestArgument>& ra, char* sr // Top level driver for models and examples generated by test_generator.py // Test driver for those generated from ml/nn/runtime/test/spec void EvaluatePreparedModel(sp<IPreparedModel>& preparedModel, std::function<bool(int)> is_ignored, const std::vector<MixedTypedExampleType>& examples) { const std::vector<MixedTypedExampleType>& examples, float fpRange = 1e-5f) { const uint32_t INPUT = 0; const uint32_t OUTPUT = 1; Loading Loading @@ -174,7 +175,7 @@ void EvaluatePreparedModel(sp<IPreparedModel>& preparedModel, std::function<bool MixedTyped filtered_test = filter(test, is_ignored); // We want "close-enough" results for float compare(filtered_golden, filtered_test); compare(filtered_golden, filtered_test, fpRange); } } Loading Loading @@ -274,7 +275,9 @@ void Execute(sp<V1_1::IDevice>& device, std::function<V1_1::Model(void)> create_ } ASSERT_NE(nullptr, preparedModel.get()); EvaluatePreparedModel(preparedModel, is_ignored, examples); // If in relaxed mode, set the error range to be 5ULP of FP16. float fpRange = !model.relaxComputationFloat32toFloat16 ? 1e-5f : 5.0f * 0.0009765625f; EvaluatePreparedModel(preparedModel, is_ignored, examples, fpRange); } } // namespace generated_tests Loading