wehub-resource-sync
141 lines
5.3 KiB
C++
141 lines
5.3 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 <initializer_list>
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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/test_util.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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#include "tensorflow/lite/types/half.h"
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namespace tflite {
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namespace {
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using ::testing::ElementsAreArray;
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class FloorOpModel : public SingleOpModel {
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public:
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FloorOpModel(std::initializer_list<int> input_shape, TensorType input_type) {
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input_ = AddInput(input_type);
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output_ = AddOutput(input_type);
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SetBuiltinOp(BuiltinOperator_FLOOR, BuiltinOptions_NONE, 0);
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BuildInterpreter({
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input_shape,
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});
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}
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int input() { return input_; }
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template <typename T>
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std::vector<T> GetOutput() {
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return ExtractVector<T>(output_);
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}
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std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
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private:
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int input_;
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int output_;
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};
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TEST(FloorOpTest, SingleDim) {
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FloorOpModel model({2}, TensorType_FLOAT32);
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model.PopulateTensor<float>(model.input(), {8.5, 0.0});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutput<float>(), ElementsAreArray({8, 0}));
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EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2}));
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}
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TEST(FloorOpTest, MultiDims) {
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FloorOpModel model({2, 1, 1, 5}, TensorType_FLOAT32);
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std::vector<float> input;
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if (AllowFp16PrecisionForFp32()) {
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input = {
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0.01, 8.01, 0.99, 9.99, 0.5, -0.01, -8.01, -0.99, -9.99, -0.5,
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};
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} else {
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input = {
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0.0001, 8.0001, 0.9999, 9.9999, 0.5,
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-0.0001, -8.0001, -0.9999, -9.9999, -0.5,
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};
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}
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model.PopulateTensor<float>(model.input(), input);
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutput<float>(),
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ElementsAreArray({0, 8, 0, 9, 0, -1, -9, -1, -10, -1}));
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EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 1, 1, 5}));
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}
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TEST(FloorOpTest, SingleDimFloat16) {
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FloorOpModel model({2}, TensorType_FLOAT16);
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model.PopulateTensor<>(model.input(), {half(8.5f), half(0.0f)});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutput<half>(), ElementsAreArray({8, 0}));
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EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2}));
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}
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TEST(FloorOpTest, MultiDimsFloat16) {
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FloorOpModel model({2, 1, 1, 5}, TensorType_FLOAT16);
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model.PopulateTensor<half>(model.input(), {
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half(0.75f),
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half(8.25f),
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half(0.49f),
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half(9.99f),
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half(0.5f),
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half(-0.25f),
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half(-8.75f),
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half(-0.99f),
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half(-9.49f),
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half(-0.5f),
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});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutput<half>(),
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ElementsAreArray({0, 8, 0, 9, 0, -1, -9, -1, -10, -1}));
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EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 1, 1, 5}));
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}
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TEST(FloorOpTest, SingleDimBFloat16) {
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FloorOpModel model({2}, TensorType_BFLOAT16);
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model.PopulateTensor<>(model.input(),
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{Eigen::bfloat16(8.5), Eigen::bfloat16(0.0)});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutput<Eigen::bfloat16>(), ElementsAreArray({8, 0}));
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EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2}));
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}
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TEST(FloorOpTest, MultiDimsBFloat16) {
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FloorOpModel model({2, 1, 1, 5}, TensorType_BFLOAT16);
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model.PopulateTensor<Eigen::bfloat16>(model.input(),
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{
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Eigen::bfloat16(1.75),
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Eigen::bfloat16(8.5),
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Eigen::bfloat16(1.49),
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Eigen::bfloat16(9.01),
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Eigen::bfloat16(1.5),
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Eigen::bfloat16(-1.25),
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Eigen::bfloat16(-8.99),
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Eigen::bfloat16(-1.99),
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Eigen::bfloat16(-9.5),
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Eigen::bfloat16(-1.5),
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});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutput<Eigen::bfloat16>(),
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ElementsAreArray({1, 8, 1, 9, 1, -2, -9, -2, -10, -2}));
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EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 1, 1, 5}));
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}
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} // namespace
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} // namespace tflite
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