wehub-resource-sync
110 lines
3.9 KiB
C++
110 lines
3.9 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 "flatbuffers/flatbuffers.h" // from @flatbuffers
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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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class LogicalOpModel : public SingleOpModel {
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public:
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LogicalOpModel(std::initializer_list<int> input1_shape,
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std::initializer_list<int> input2_shape, BuiltinOperator op) {
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input1_ = AddInput(TensorType_BOOL);
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input2_ = AddInput(TensorType_BOOL);
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output_ = AddOutput(TensorType_BOOL);
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ConfigureBuiltinOp(op);
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BuildInterpreter({input1_shape, input2_shape});
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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<bool> GetOutput() { return ExtractVector<bool>(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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void ConfigureBuiltinOp(BuiltinOperator op) {
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switch (op) {
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case BuiltinOperator_LOGICAL_OR: {
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SetBuiltinOp(op, BuiltinOptions_LogicalOrOptions,
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CreateLogicalOrOptions(builder_).Union());
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break;
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}
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case BuiltinOperator_LOGICAL_AND: {
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SetBuiltinOp(op, BuiltinOptions_LogicalAndOptions,
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CreateLogicalAndOptions(builder_).Union());
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break;
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}
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default: { FAIL() << "We shouldn't get here."; }
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}
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}
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};
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TEST(LogicalTest, LogicalOr) {
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LogicalOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, BuiltinOperator_LOGICAL_OR);
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model.PopulateTensor<bool>(model.input1(), {true, false, false, true});
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model.PopulateTensor<bool>(model.input2(), {true, false, true, false});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutput(), ElementsAre(true, false, true, true));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 1, 4));
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}
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TEST(LogicalTest, BroadcastLogicalOr) {
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LogicalOpModel model({1, 1, 1, 4}, {1, 1, 1, 1}, BuiltinOperator_LOGICAL_OR);
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model.PopulateTensor<bool>(model.input1(), {true, false, false, true});
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model.PopulateTensor<bool>(model.input2(), {false});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutput(), ElementsAre(true, false, false, true));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 1, 4));
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}
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TEST(LogicalTest, LogicalAnd) {
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LogicalOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, BuiltinOperator_LOGICAL_AND);
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model.PopulateTensor<bool>(model.input1(), {true, false, false, true});
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model.PopulateTensor<bool>(model.input2(), {true, false, true, false});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutput(), ElementsAre(true, false, false, false));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 1, 4));
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}
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TEST(LogicalTest, BroadcastLogicalAnd) {
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LogicalOpModel model({1, 1, 1, 4}, {1, 1, 1, 1}, BuiltinOperator_LOGICAL_AND);
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model.PopulateTensor<bool>(model.input1(), {true, false, false, true});
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model.PopulateTensor<bool>(model.input2(), {true});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutput(), ElementsAre(true, false, false, true));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 1, 4));
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}
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} // namespace
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} // namespace tflite
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