Implements RANDOM_MULTINOMIAL CTS/VTS tests.
Bug: 113562775
Test: VtsHalNeuralnetworksV1_2TargetTest --hal_service_instance=android.hardware.neuralnetworks@1.2::IDevice/sample-all
Change-Id: I3d39d056bf5034e01ce9c5c5e3f416a63d7ec8eb
Merged-In: I3d39d056bf5034e01ce9c5c5e3f416a63d7ec8eb
(cherry picked from commit 941d61a62d52b3b5c526c88ace1fc0e538364c71)
diff --git a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
index b8046c7..cc19978 100644
--- a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
+++ b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
@@ -36,16 +36,17 @@
namespace generated_tests {
using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
+using ::test_helper::compare;
+using ::test_helper::expectMultinomialDistributionWithinTolerance;
using ::test_helper::filter;
+using ::test_helper::Float32Operands;
using ::test_helper::for_all;
using ::test_helper::for_each;
-using ::test_helper::resize_accordingly;
-using ::test_helper::MixedTyped;
-using ::test_helper::MixedTypedExampleType;
-using ::test_helper::Float32Operands;
using ::test_helper::Int32Operands;
+using ::test_helper::MixedTyped;
+using ::test_helper::MixedTypedExample;
using ::test_helper::Quant8Operands;
-using ::test_helper::compare;
+using ::test_helper::resize_accordingly;
template <typename T>
void copy_back_(MixedTyped* dst, const std::vector<RequestArgument>& ra, char* src) {
@@ -66,7 +67,7 @@
// 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, float fpAtol = 1e-5f,
+ const std::vector<MixedTypedExample>& examples, float fpAtol = 1e-5f,
float fpRtol = 1e-5f) {
const uint32_t INPUT = 0;
const uint32_t OUTPUT = 1;
@@ -75,8 +76,8 @@
for (auto& example : examples) {
SCOPED_TRACE(example_no++);
- const MixedTyped& inputs = example.first;
- const MixedTyped& golden = example.second;
+ const MixedTyped& inputs = example.operands.first;
+ const MixedTyped& golden = example.operands.second;
std::vector<RequestArgument> inputs_info, outputs_info;
uint32_t inputSize = 0, outputSize = 0;
@@ -176,12 +177,15 @@
// We want "close-enough" results for float
compare(filtered_golden, filtered_test, fpAtol, fpRtol);
+
+ if (example.expectedMultinomialDistributionTolerance > 0) {
+ expectMultinomialDistributionWithinTolerance(test, example);
+ }
}
}
void Execute(const sp<V1_0::IDevice>& device, std::function<V1_0::Model(void)> create_model,
- std::function<bool(int)> is_ignored,
- const std::vector<MixedTypedExampleType>& examples) {
+ std::function<bool(int)> is_ignored, const std::vector<MixedTypedExample>& examples) {
V1_0::Model model = create_model();
// see if service can handle model
@@ -225,8 +229,7 @@
}
void Execute(const sp<V1_1::IDevice>& device, std::function<V1_1::Model(void)> create_model,
- std::function<bool(int)> is_ignored,
- const std::vector<MixedTypedExampleType>& examples) {
+ std::function<bool(int)> is_ignored, const std::vector<MixedTypedExample>& examples) {
V1_1::Model model = create_model();
// see if service can handle model
@@ -277,8 +280,7 @@
// TODO: Reduce code duplication.
void Execute(const sp<V1_2::IDevice>& device, std::function<V1_2::Model(void)> create_model,
- std::function<bool(int)> is_ignored,
- const std::vector<MixedTypedExampleType>& examples) {
+ std::function<bool(int)> is_ignored, const std::vector<MixedTypedExample>& examples) {
V1_2::Model model = create_model();
// see if service can handle model