summaryrefslogtreecommitdiff
path: root/tests/nnapi/nnapi_test_generator/android-p/include/TestHarness.h
diff options
context:
space:
mode:
Diffstat (limited to 'tests/nnapi/nnapi_test_generator/android-p/include/TestHarness.h')
-rw-r--r--tests/nnapi/nnapi_test_generator/android-p/include/TestHarness.h209
1 files changed, 209 insertions, 0 deletions
diff --git a/tests/nnapi/nnapi_test_generator/android-p/include/TestHarness.h b/tests/nnapi/nnapi_test_generator/android-p/include/TestHarness.h
new file mode 100644
index 000000000..1fcb0d661
--- /dev/null
+++ b/tests/nnapi/nnapi_test_generator/android-p/include/TestHarness.h
@@ -0,0 +1,209 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * 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.
+ */
+
+/* Header-only library for various helpers of test harness
+ * See frameworks/ml/nn/runtime/test/TestGenerated.cpp for how this is used.
+ */
+#ifndef ANDROID_ML_NN_TOOLS_TEST_GENERATOR_TEST_HARNESS_H
+#define ANDROID_ML_NN_TOOLS_TEST_GENERATOR_TEST_HARNESS_H
+
+#include <gtest/gtest.h>
+
+#include <cmath>
+#include <functional>
+#include <map>
+#include <tuple>
+#include <vector>
+
+namespace generated_tests {
+
+constexpr const size_t gMaximumNumberOfErrorMessages = 10;
+
+typedef std::map<int, std::vector<float>> Float32Operands;
+typedef std::map<int, std::vector<int32_t>> Int32Operands;
+typedef std::map<int, std::vector<uint8_t>> Quant8Operands;
+typedef std::tuple<Float32Operands, // ANEURALNETWORKS_TENSOR_FLOAT32
+ Int32Operands, // ANEURALNETWORKS_TENSOR_INT32
+ Quant8Operands // ANEURALNETWORKS_TENSOR_QUANT8_ASYMM
+ >
+ MixedTyped;
+typedef std::pair<MixedTyped, MixedTyped> MixedTypedExampleType;
+
+template <typename T>
+struct MixedTypedIndex {};
+
+template <>
+struct MixedTypedIndex<float> {
+ static constexpr size_t index = 0;
+};
+template <>
+struct MixedTypedIndex<int32_t> {
+ static constexpr size_t index = 1;
+};
+template <>
+struct MixedTypedIndex<uint8_t> {
+ static constexpr size_t index = 2;
+};
+
+// Go through all index-value pairs of a given input type
+template <typename T>
+inline void for_each(const MixedTyped& idx_and_data,
+ std::function<void(int, const std::vector<T>&)> execute) {
+ for (auto& i : std::get<MixedTypedIndex<T>::index>(idx_and_data)) {
+ execute(i.first, i.second);
+ }
+}
+
+// non-const variant of for_each
+template <typename T>
+inline void for_each(MixedTyped& idx_and_data,
+ std::function<void(int, std::vector<T>&)> execute) {
+ for (auto& i : std::get<MixedTypedIndex<T>::index>(idx_and_data)) {
+ execute(i.first, i.second);
+ }
+}
+
+// internal helper for for_all
+template <typename T>
+inline void for_all_internal(
+ MixedTyped& idx_and_data,
+ std::function<void(int, void*, size_t)> execute_this) {
+ for_each<T>(idx_and_data, [&execute_this](int idx, std::vector<T>& m) {
+ execute_this(idx, static_cast<void*>(m.data()), m.size() * sizeof(T));
+ });
+}
+
+// Go through all index-value pairs of all input types
+// expects a functor that takes (int index, void *raw data, size_t sz)
+inline void for_all(MixedTyped& idx_and_data,
+ std::function<void(int, void*, size_t)> execute_this) {
+ for_all_internal<float>(idx_and_data, execute_this);
+ for_all_internal<int32_t>(idx_and_data, execute_this);
+ for_all_internal<uint8_t>(idx_and_data, execute_this);
+}
+
+// Const variant of internal helper for for_all
+template <typename T>
+inline void for_all_internal(
+ const MixedTyped& idx_and_data,
+ std::function<void(int, const void*, size_t)> execute_this) {
+ for_each<T>(idx_and_data, [&execute_this](int idx, const std::vector<T>& m) {
+ execute_this(idx, static_cast<const void*>(m.data()), m.size() * sizeof(T));
+ });
+}
+
+// Go through all index-value pairs (const variant)
+// expects a functor that takes (int index, const void *raw data, size_t sz)
+inline void for_all(
+ const MixedTyped& idx_and_data,
+ std::function<void(int, const void*, size_t)> execute_this) {
+ for_all_internal<float>(idx_and_data, execute_this);
+ for_all_internal<int32_t>(idx_and_data, execute_this);
+ for_all_internal<uint8_t>(idx_and_data, execute_this);
+}
+
+// Helper template - resize test output per golden
+template <typename ty, size_t tuple_index>
+void resize_accordingly_(const MixedTyped& golden, MixedTyped& test) {
+ std::function<void(int, const std::vector<ty>&)> execute =
+ [&test](int index, const std::vector<ty>& m) {
+ auto& t = std::get<tuple_index>(test);
+ t[index].resize(m.size());
+ };
+ for_each<ty>(golden, execute);
+}
+
+inline void resize_accordingly(const MixedTyped& golden, MixedTyped& test) {
+ resize_accordingly_<float, 0>(golden, test);
+ resize_accordingly_<int32_t, 1>(golden, test);
+ resize_accordingly_<uint8_t, 2>(golden, test);
+}
+
+template <typename ty, size_t tuple_index>
+void filter_internal(const MixedTyped& golden, MixedTyped* filtered,
+ std::function<bool(int)> is_ignored) {
+ for_each<ty>(golden,
+ [filtered, &is_ignored](int index, const std::vector<ty>& m) {
+ auto& g = std::get<tuple_index>(*filtered);
+ if (!is_ignored(index)) g[index] = m;
+ });
+}
+
+inline MixedTyped filter(const MixedTyped& golden,
+ std::function<bool(int)> is_ignored) {
+ MixedTyped filtered;
+ filter_internal<float, 0>(golden, &filtered, is_ignored);
+ filter_internal<int32_t, 1>(golden, &filtered, is_ignored);
+ filter_internal<uint8_t, 2>(golden, &filtered, is_ignored);
+ return filtered;
+}
+
+// Compare results
+#define VECTOR_TYPE(x) \
+ typename std::tuple_element<x, MixedTyped>::type::mapped_type
+#define VALUE_TYPE(x) VECTOR_TYPE(x)::value_type
+template <size_t tuple_index>
+void compare_(
+ const MixedTyped& golden, const MixedTyped& test,
+ std::function<void(VALUE_TYPE(tuple_index), VALUE_TYPE(tuple_index))>
+ cmp) {
+ for_each<VALUE_TYPE(tuple_index)>(
+ golden,
+ [&test, &cmp](int index, const VECTOR_TYPE(tuple_index) & m) {
+ const auto& test_operands = std::get<tuple_index>(test);
+ const auto& test_ty = test_operands.find(index);
+ ASSERT_NE(test_ty, test_operands.end());
+ for (unsigned int i = 0; i < m.size(); i++) {
+ SCOPED_TRACE(testing::Message()
+ << "When comparing element " << i);
+ cmp(m[i], test_ty->second[i]);
+ }
+ });
+}
+#undef VALUE_TYPE
+#undef VECTOR_TYPE
+inline void compare(const MixedTyped& golden, const MixedTyped& test, float fpRange = 1e-5f) {
+ size_t totalNumberOfErrors = 0;
+ compare_<0>(golden, test, [&totalNumberOfErrors, fpRange](float g, float t) {
+ if (totalNumberOfErrors < gMaximumNumberOfErrorMessages) {
+ EXPECT_NEAR(g, t, fpRange);
+ }
+ if (std::abs(g - t) > fpRange) {
+ totalNumberOfErrors++;
+ }
+ });
+ compare_<1>(golden, test, [&totalNumberOfErrors](int32_t g, int32_t t) {
+ if (totalNumberOfErrors < gMaximumNumberOfErrorMessages) {
+ EXPECT_EQ(g, t);
+ }
+ if (g != t) {
+ totalNumberOfErrors++;
+ }
+ });
+ compare_<2>(golden, test, [&totalNumberOfErrors](uint8_t g, uint8_t t) {
+ if (totalNumberOfErrors < gMaximumNumberOfErrorMessages) {
+ EXPECT_NEAR(g, t, 1);
+ }
+ if (std::abs(g - t) > 1) {
+ totalNumberOfErrors++;
+ }
+ });
+ EXPECT_EQ(size_t{0}, totalNumberOfErrors);
+}
+
+}; // namespace generated_tests
+
+#endif // ANDROID_ML_NN_TOOLS_TEST_GENERATOR_TEST_HARNESS_H