wehub-resource-sync
220 lines
9.0 KiB
C++
220 lines
9.0 KiB
C++
/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include <math.h>
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#include <stdint.h>
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#include <vector>
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#include <gmock/gmock.h>
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#include <gtest/gtest.h>
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#include "tensorflow/lite/kernels/internal/test_util.h"
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#include "tensorflow/lite/kernels/test_util.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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namespace tflite {
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namespace {
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using ::testing::ElementsAre;
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using ::testing::ElementsAreArray;
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template <typename T>
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class PowOpModel : public SingleOpModel {
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public:
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PowOpModel(const TensorData& input1, const TensorData& input2,
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const TensorData& output, bool allocate = true) {
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input1_ = AddInput(input1);
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input2_ = AddInput(input2);
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output_ = AddOutput(output);
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SetBuiltinOp(BuiltinOperator_POW, BuiltinOptions_PowOptions,
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CreatePowOptions(builder_).Union());
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BuildInterpreter({GetShape(input1_), GetShape(input2_)},
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/*num_threads=*/-1, /*allow_fp32_relax_to_fp16=*/false,
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/*apply_delegate=*/true,
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/*allocate_and_delegate=*/allocate);
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}
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int input1() { return input1_; }
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int input2() { return input2_; }
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std::vector<T> GetOutput() { return ExtractVector<T>(output_); }
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std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
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private:
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int input1_;
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int input2_;
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int output_;
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};
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template <typename T>
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class FloatPowTest : public ::testing::Test {};
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using FloatPowTestTypes = ::testing::Types<float, half, Eigen::bfloat16>;
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TYPED_TEST_SUITE(FloatPowTest, FloatPowTestTypes);
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TEST(PowOpModel, Simple) {
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PowOpModel<int32_t> model({TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {}});
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model.PopulateTensor<int32_t>(model.input1(), {12, 2, 7, 8});
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model.PopulateTensor<int32_t>(model.input2(), {1, 2, 3, 1});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
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EXPECT_THAT(model.GetOutput(), ElementsAre(12, 4, 343, 8));
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}
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TEST(PowOpModel, NegativeAndZeroValue) {
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PowOpModel<int32_t> model({TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {}});
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model.PopulateTensor<int32_t>(model.input1(), {0, 2, -7, 8});
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model.PopulateTensor<int32_t>(model.input2(), {1, 2, 3, 0});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
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EXPECT_THAT(model.GetOutput(), ElementsAre(0, 4, -343, 1));
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}
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TYPED_TEST(FloatPowTest, Float) {
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using T = TypeParam;
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PowOpModel<T> model({GetTensorType<T>(), {1, 2, 2, 1}},
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{GetTensorType<T>(), {1, 2, 2, 1}},
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{GetTensorType<T>(), {}}, /*allocate=*/false);
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TFLITE_ALLOCATE_AND_CHECK(T, &model);
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model.template PopulateTensor<T>(model.input1(), {0.3, 0.4, 0.7, 5.8});
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model.template PopulateTensor<T>(model.input2(), {0.5, 2.7, 3.1, 3.2});
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TFLITE_INVOKE_AND_CHECK(T, &model);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray(
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ArrayFloatNear({0.5477226, 0.08424846, 0.33098164, 277.313},
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/*max_abs_err=*/1e-3,
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/*fp16_max_abs_err=*/1e-2)));
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}
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TYPED_TEST(FloatPowTest, NegativeFloatTest) {
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using T = TypeParam;
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PowOpModel<T> model({GetTensorType<T>(), {1, 2, 2, 1}},
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{GetTensorType<T>(), {1, 2, 2, 1}},
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{GetTensorType<T>(), {}}, /*allocate=*/false);
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TFLITE_ALLOCATE_AND_CHECK(T, &model);
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model.template PopulateTensor<T>(model.input1(), {0.3, 0.4, 0.7, 5.8});
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model.template PopulateTensor<T>(model.input2(), {0.5, -2.7, 3.1, -3.2});
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TFLITE_INVOKE_AND_CHECK(T, &model);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray(
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ArrayFloatNear({0.5477226, 11.869653, 0.33098164, 0.003606},
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/*max_abs_err=*/1e-3,
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/*fp16_max_abs_err=*/1e-2)));
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}
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TEST(PowOpModel, BroadcastTest) {
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PowOpModel<int32_t> model({TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {1}}, {TensorType_INT32, {}});
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model.PopulateTensor<int32_t>(model.input1(), {12, 2, 7, 8});
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model.PopulateTensor<int32_t>(model.input2(), {4});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
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EXPECT_THAT(model.GetOutput(), ElementsAre(20736, 16, 2401, 4096));
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}
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TEST(PowOpModel, BroadcastRankFive) {
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PowOpModel<int32_t> model({TensorType_INT32, {1, 2, 1, 2, 2}},
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{TensorType_INT32, {1, 1, 2}},
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{TensorType_INT32, {}});
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model.PopulateTensor<int32_t>(model.input1(), {2, 3, 4, 5, 2, 3, 4, 5});
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model.PopulateTensor<int32_t>(model.input2(), {2, 3});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 1, 2, 2));
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EXPECT_THAT(model.GetOutput(), ElementsAre(4, 27, 16, 125, 4, 27, 16, 125));
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}
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TYPED_TEST(FloatPowTest, BroadcastFloatTest) {
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using T = TypeParam;
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PowOpModel<T> model({GetTensorType<T>(), {1, 2, 2, 1}},
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{GetTensorType<T>(), {1}}, {GetTensorType<T>(), {}},
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/*allocate=*/false);
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TFLITE_ALLOCATE_AND_CHECK(T, &model);
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model.template PopulateTensor<T>(model.input1(), {12, 2, 7, 8});
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model.template PopulateTensor<T>(model.input2(), {4});
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TFLITE_INVOKE_AND_CHECK(T, &model);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray(ArrayFloatNear({20736, 16, 2401, 4096},
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NumericLimits<T>::epsilon())));
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}
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template <typename T>
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void CalculateTrueResults(const std::vector<T>& input_data, T exponent,
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int flat_size, std::vector<T>* output_data) {
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for (int i = 0; i < flat_size; ++i) {
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output_data->at(i) = static_cast<T>(std::pow(
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static_cast<float>(input_data[i]), static_cast<float>(exponent)));
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}
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}
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TYPED_TEST(FloatPowTest, FloatSingleIntegerExponentTest) {
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using T = TypeParam;
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PowOpModel<T> model({GetTensorType<T>(), {1, 2, 2, 1}},
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{GetTensorType<T>(), {1}}, {GetTensorType<T>(), {}},
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/*allocate=*/false);
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TFLITE_ALLOCATE_AND_CHECK(T, &model);
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const int input_size = 1 * 2 * 2 * 1;
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for (int i = 1; i < 20; ++i) {
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std::vector<float> input_data(input_size);
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for (int index = 0; index < input_size; ++index) {
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// For exponent is float case, if base < 0, we will result in nan, so
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// we only populate positive base.
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input_data[index] = UniformRandomFloat(0, 1.5);
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}
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model.template PopulateTensor<T>(model.input1(), ToVector<T>(input_data));
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float exponent = static_cast<float>(i);
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// Random deviate exponent, e.g., 1.99999 or 2.00001.
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exponent += UniformRandomInt(-1, 1) * 1e-5;
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model.template PopulateTensor<T>(model.input2(), ToVector<T>({exponent}));
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TFLITE_INVOKE_AND_CHECK(T, &model);
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std::vector<T> expected_output(input_size);
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CalculateTrueResults<T>(ToVector<T>(input_data), static_cast<T>(exponent),
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input_size, &expected_output);
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EXPECT_THAT(model.GetOutput(), ElementsAreArray(ArrayFloatNear(
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ToVector<float>(expected_output),
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/*max_abs_err=*/1e-2,
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/*fp16_max_abs_err=*/1e-2)));
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}
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}
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TEST(PowOpModel, IntSingleIntegerExponentTest) {
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PowOpModel<int32_t> model({TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {1}}, {TensorType_INT32, {}});
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const int input_size = 1 * 2 * 2 * 1;
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for (int i = 1; i < 20; ++i) {
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std::vector<int32_t> input_data(input_size);
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for (int index = 0; index < input_size; ++index) {
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input_data[index] = UniformRandomInt(-2, -2);
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}
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model.PopulateTensor<int32_t>(model.input1(), input_data);
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int exponent = i;
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model.PopulateTensor<int32_t>(model.input2(), {exponent});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
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std::vector<int32_t> output_data(input_size);
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CalculateTrueResults(input_data, exponent, input_size, &output_data);
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EXPECT_THAT(model.GetOutput(), ElementsAreArray(output_data));
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}
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}
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} // namespace
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} // namespace tflite
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