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-rw-r--r--runtimes/nn/common/operations/internal/tensor_utils_test.cc198
1 files changed, 0 insertions, 198 deletions
diff --git a/runtimes/nn/common/operations/internal/tensor_utils_test.cc b/runtimes/nn/common/operations/internal/tensor_utils_test.cc
deleted file mode 100644
index b68982164..000000000
--- a/runtimes/nn/common/operations/internal/tensor_utils_test.cc
+++ /dev/null
@@ -1,198 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- * Copyright 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.
- */
-
-#include "gmock/gmock-matchers.h"
-#include "gtest/gtest.h"
-#include "tensor_utils.h"
-
-namespace nnfw {
-namespace rt {
-namespace tensor_utils {
-
-namespace {
-
-using ::testing::FloatNear;
-using ::testing::Matcher;
-
-std::vector<Matcher<float>> ArrayFloatNear(const std::vector<float>& values,
- float max_abs_error=1.e-6) {
- std::vector<Matcher<float>> matchers;
- matchers.reserve(values.size());
- for (const float& v : values) {
- matchers.emplace_back(FloatNear(v, max_abs_error));
- }
- return matchers;
-}
-
-} // anonymous namespace
-
-TEST(uKernels, ClipTest) {
- constexpr int kVectorSize = 10;
- constexpr float kAbsLimit = 2.0;
- static float input[kVectorSize] = {0.0, -0.5, 1.0, -1.5, 2.0,
- -2.5, 3.0, -3.5, 4.0, -4.5};
- std::vector<float> output(kVectorSize);
- ClipVector(input, kVectorSize, kAbsLimit, output.data());
- EXPECT_THAT(output,
- ElementsAreArray(ArrayFloatNear(
- {0.0, -0.5, 1.0, -1.5, 2.0, -2.0, 2.0, -2.0, 2.0, -2.0})));
-}
-
-TEST(uKernels, MatrixBatchVectorMultiplyAccumulateTest) {
- constexpr int kRow = 3;
- constexpr int kCol = 4;
- constexpr int kBatch = 2;
- static float matrix[kRow * kCol] = {1.0, 2.0, 3.0, 4.0, //
- -1.0, -2.0, -3.0, -4.0, //
- 1.0, -2.0, 3.0, -4.0};
- static float vector[kCol * kBatch] = {1.0, -1.0, 1.0, -1.0, //
- 2.0, -2.0, 2.0, -2.0};
- std::vector<float> output(kRow * kBatch);
- std::fill(output.begin(), output.end(), 3.0);
- MatrixBatchVectorMultiplyAccumulate(matrix, kRow, kCol, vector, kBatch,
- output.data(), /*result_stride=*/1);
- EXPECT_THAT(output, ElementsAreArray(ArrayFloatNear({1., 5., 13., //
- -1., 7., 23.})));
-}
-
-TEST(uKernels, VectorVectorCwiseProductTest) {
- constexpr int kVectorSize = 10;
- static float input1[kVectorSize] = {0.0, -0.5, 1.0, -1.5, 2.0,
- -2.5, 3.0, -3.5, 4.0, -4.5};
- static float input2[kVectorSize] = {0.1, -0.1, 0.1, -0.1, 0.1,
- -0.1, 0.1, -0.1, 0.1, -0.1};
- std::vector<float> output(kVectorSize);
- VectorVectorCwiseProduct(input1, input2, kVectorSize, output.data());
- EXPECT_THAT(output,
- ElementsAreArray(ArrayFloatNear(
- {0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45})));
-}
-
-TEST(uKernels, VectorVectorCwiseProductAccumulateTest) {
- constexpr int kVectorSize = 10;
- static float input1[kVectorSize] = {0.0, -0.5, 1.0, -1.5, 2.0,
- -2.5, 3.0, -3.5, 4.0, -4.5};
- static float input2[kVectorSize] = {0.1, -0.1, 0.1, -0.1, 0.1,
- -0.1, 0.1, -0.1, 0.1, -0.1};
- std::vector<float> output(kVectorSize);
- std::fill(output.begin(), output.end(), 1.0);
- VectorVectorCwiseProductAccumulate(input1, input2, kVectorSize,
- output.data());
- EXPECT_THAT(output,
- ElementsAreArray(ArrayFloatNear(
- {1.0, 1.05, 1.1, 1.15, 1.2, 1.25, 1.3, 1.35, 1.4, 1.45})));
-}
-
-TEST(uKernels, VectorBatchVectorAssignTest) {
- constexpr int kVectorSize = 5;
- constexpr int kBatchSize = 3;
- static float input[kVectorSize] = {0.0, -0.5, 1.0, -1.5, 2.0};
- std::vector<float> output(kVectorSize * kBatchSize);
- VectorBatchVectorAssign(input, kVectorSize, kBatchSize, output.data());
- EXPECT_THAT(output, ElementsAreArray(ArrayFloatNear(
- {0.0, -0.5, 1.0, -1.5, 2.0, 0.0, -0.5, 1.0, -1.5, 2.0,
- 0.0, -0.5, 1.0, -1.5, 2.0})));
-}
-
-TEST(uKernels, ApplySigmoidToVectorTest) {
- constexpr int kVectorSize = 5;
- static float input[kVectorSize] = {0.0, -0.5, 1.0, -1.5, 2.0};
- std::vector<float> output(kVectorSize);
- ApplySigmoidToVector(input, kVectorSize, output.data());
- EXPECT_THAT(output, ElementsAreArray(ArrayFloatNear(
- {0.5, 0.377541, 0.731059, 0.182426, 0.880797})));
-}
-
-TEST(uKernels, ApplyActivationToVectorTest) {
- constexpr int kVectorSize = 5;
- static float input[kVectorSize] = {0.0, -0.5, 1.0, -1.5, 2.0};
- std::vector<float> output(kVectorSize);
- ApplyActivationToVector(input, kVectorSize, kActivationRelu, output.data());
- EXPECT_THAT(output,
- ElementsAreArray(ArrayFloatNear({0.0, 0.0, 1.0, 0.0, 2.0})));
-
- ApplyActivationToVector(input, kVectorSize, kActivationTanh, output.data());
- EXPECT_THAT(output, ElementsAreArray(ArrayFloatNear(
- {0.0, -0.462117, 0.761594, -0.905148, 0.964028})));
-}
-
-TEST(uKernels, CopyVectorTest) {
- constexpr int kVectorSize = 5;
- static float input[kVectorSize] = {0.0, -0.5, 1.0, -1.5, 2.0};
- std::vector<float> output(kVectorSize);
- CopyVector(input, kVectorSize, output.data());
- EXPECT_THAT(output,
- ElementsAreArray(ArrayFloatNear({0.0, -0.5, 1.0, -1.5, 2.0})));
-}
-
-TEST(uKernels, Sub1VectorTest) {
- constexpr int kVectorSize = 5;
- static float input[kVectorSize] = {0.0, -0.5, 1.0, -1.5, 2.0};
- std::vector<float> output(kVectorSize);
- Sub1Vector(input, kVectorSize, output.data());
- EXPECT_THAT(output,
- ElementsAreArray(ArrayFloatNear({1.0, 1.5, 0.0, 2.5, -1.0})));
-}
-
-TEST(uKernels, ZeroVectorTest) {
- constexpr int kVectorSize = 5;
- std::vector<float> output(kVectorSize);
- ZeroVector(output.data(), kVectorSize);
- EXPECT_THAT(output,
- ElementsAreArray(ArrayFloatNear({0.0, 0.0, 0.0, 0.0, 0.0})));
-}
-
-TEST(uKernels, BatchVectorBatchVectorDotProductTest) {
- constexpr int kVectorSize = 5;
- constexpr int kBatch = 2;
- static float input1[kVectorSize * kBatch] = {0.0, -0.5, 1.0, -1.5, 2.0,
- -2.5, 3.0, -3.5, 4.0, -4.5};
- static float input2[kVectorSize * kBatch] = {0.1, -0.1, 0.1, -0.1, 0.1,
- -0.1, 0.1, -0.1, 0.1, -0.1};
- std::vector<float> output(kBatch);
- BatchVectorBatchVectorDotProduct(input1, input2, kVectorSize, kBatch,
- output.data(), /*result_stride=*/1);
- EXPECT_THAT(output, ElementsAreArray(ArrayFloatNear({0.5, 1.75})));
-}
-
-TEST(uKernels, VectorShiftLeftTest) {
- constexpr int kVectorSize = 5;
- static float input[kVectorSize] = {0.0, -0.5, 1.0, -1.5, 2.0};
- std::vector<float> result(kVectorSize);
- VectorShiftLeft(input, kVectorSize, 3.0);
- result.assign(input, input + kVectorSize);
- EXPECT_THAT(result,
- ElementsAreArray(ArrayFloatNear({-0.5, 1.0, -1.5, 2.0, 3.0})));
-}
-
-TEST(uKernels, ReductionSumVectorTest) {
- constexpr int kInputVectorSize = 10;
- constexpr int kOutputVectorSize = 5;
- constexpr int kReductionSize = 2;
- static float input[kInputVectorSize] = {0.0, -0.5, 1.0, -1.5, 2.0,
- 0.0, -0.5, 1.0, 1.0, 2.0};
- std::vector<float> result(kOutputVectorSize);
- ReductionSumVector(input,
- /*input_stride=*/1, result.data(), kOutputVectorSize,
- kReductionSize);
- EXPECT_THAT(result,
- ElementsAreArray(ArrayFloatNear({-0.5, -0.5, 2.0, 0.5, 3.0})));
-}
-
-} // namespace tensor_utils
-} // namespace rt
-} // namespace nnfw
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