703 lines
28 KiB
C++
703 lines
28 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 <gtest/gtest.h>
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#include "tensorflow/lite/interpreter.h"
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#include "tensorflow/lite/kernels/register.h"
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#include "tensorflow/lite/kernels/test_util.h"
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#include "tensorflow/lite/model.h"
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namespace tflite {
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namespace {
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using ::testing::ElementsAre;
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class ComparisonOpModel : public SingleOpModel {
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public:
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ComparisonOpModel(std::initializer_list<int> input1_shape,
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std::initializer_list<int> input2_shape,
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TensorType input_type, BuiltinOperator op) {
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input1_ = AddInput(input_type);
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input2_ = AddInput(input_type);
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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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ComparisonOpModel(const TensorData& input1, const TensorData& input2,
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TensorType input_type, BuiltinOperator op) {
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input1_ = AddInput(input1);
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input2_ = AddInput(input2);
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output_ = AddOutput(TensorType_BOOL);
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ConfigureBuiltinOp(op);
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BuildInterpreter({GetShape(input1_), GetShape(input2_)});
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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_EQUAL: {
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SetBuiltinOp(op, BuiltinOptions_EqualOptions,
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CreateEqualOptions(builder_).Union());
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break;
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}
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case BuiltinOperator_NOT_EQUAL: {
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SetBuiltinOp(op, BuiltinOptions_NotEqualOptions,
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CreateNotEqualOptions(builder_).Union());
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break;
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}
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case BuiltinOperator_GREATER: {
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SetBuiltinOp(op, BuiltinOptions_GreaterOptions,
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CreateGreaterOptions(builder_).Union());
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break;
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}
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case BuiltinOperator_GREATER_EQUAL: {
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SetBuiltinOp(op, BuiltinOptions_GreaterEqualOptions,
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CreateGreaterEqualOptions(builder_).Union());
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break;
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}
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case BuiltinOperator_LESS: {
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SetBuiltinOp(op, BuiltinOptions_LessOptions,
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CreateLessOptions(builder_).Union());
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break;
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}
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case BuiltinOperator_LESS_EQUAL: {
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SetBuiltinOp(op, BuiltinOptions_LessEqualOptions,
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CreateLessEqualOptions(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(ComparisonsTest, EqualBool) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_BOOL,
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BuiltinOperator_EQUAL);
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model.PopulateTensor<bool>(model.input1(), {true, false, true, false});
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model.PopulateTensor<bool>(model.input2(), {true, true, false, false});
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model.Invoke();
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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(ComparisonsTest, EqualFloat) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_FLOAT32,
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BuiltinOperator_EQUAL);
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model.PopulateTensor<float>(model.input1(), {0.1, 0.9, 0.7, 0.3});
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model.PopulateTensor<float>(model.input2(), {0.1, 0.2, 0.6, 0.5});
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model.Invoke();
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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(ComparisonsTest, EqualInt) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_INT32,
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BuiltinOperator_EQUAL);
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model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3});
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model.PopulateTensor<int>(model.input2(), {1, 2, 7, 5});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAre(false, false, true, false));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 1, 4));
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}
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TEST(ComparisonsTest, EqualBroadcast) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 1}, TensorType_INT32,
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BuiltinOperator_EQUAL);
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model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3});
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model.PopulateTensor<int>(model.input2(), {7});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAre(false, false, true, false));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 1, 4));
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}
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TEST(ComparisonsTest, EqualBroadcastTwoD) {
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ComparisonOpModel model({1, 1, 2, 4}, {1, 1, 1, 4}, TensorType_INT32,
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BuiltinOperator_EQUAL);
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model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3, 2, 4, 2, 8});
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model.PopulateTensor<int>(model.input2(), {7, 1, 2, 4});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAre(false, false, false, false, false,
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false, true, false));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 2, 4));
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}
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TEST(ComparisonsTest, NotEqualBool) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_BOOL,
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BuiltinOperator_NOT_EQUAL);
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model.PopulateTensor<bool>(model.input1(), {true, false, true, false});
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model.PopulateTensor<bool>(model.input2(), {true, true, false, false});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAre(false, true, true, false));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 1, 4));
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}
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TEST(ComparisonsTest, NotEqualFloat) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_FLOAT32,
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BuiltinOperator_NOT_EQUAL);
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model.PopulateTensor<float>(model.input1(), {0.1, 0.9, 0.7, 0.3});
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model.PopulateTensor<float>(model.input2(), {0.1, 0.2, 0.6, 0.5});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAre(false, true, true, true));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 1, 4));
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}
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TEST(ComparisonsTest, NotEqualInt) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_INT32,
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BuiltinOperator_NOT_EQUAL);
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model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3});
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model.PopulateTensor<int>(model.input2(), {1, 2, 7, 5});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAre(true, true, false, true));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 1, 4));
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}
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TEST(ComparisonsTest, NotEqualBroadcast) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 1}, TensorType_INT32,
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BuiltinOperator_NOT_EQUAL);
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model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3});
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model.PopulateTensor<int>(model.input2(), {7});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAre(true, true, false, true));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 1, 4));
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}
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TEST(ComparisonsTest, NotEqualBroadcastTwoD) {
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ComparisonOpModel model({1, 1, 2, 4}, {1, 1, 1, 4}, TensorType_INT32,
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BuiltinOperator_NOT_EQUAL);
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model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3, 2, 4, 2, 8});
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model.PopulateTensor<int>(model.input2(), {7, 1, 2, 4});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(),
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ElementsAre(true, true, true, true, true, true, false, true));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 2, 4));
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}
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TEST(ComparisonsTest, GreaterFloat) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_FLOAT32,
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BuiltinOperator_GREATER);
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model.PopulateTensor<float>(model.input1(), {0.1, 0.9, 0.7, 0.3});
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model.PopulateTensor<float>(model.input2(), {0.1, 0.2, 0.6, 0.5});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAre(false, true, true, false));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 1, 4));
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}
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TEST(ComparisonsTest, GreaterInt) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_INT32,
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BuiltinOperator_GREATER);
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model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3});
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model.PopulateTensor<int>(model.input2(), {1, 2, 7, 5});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAre(false, true, false, false));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 1, 4));
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}
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TEST(ComparisonsTest, GreaterBroadcast) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 1}, TensorType_INT32,
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BuiltinOperator_GREATER);
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model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3});
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model.PopulateTensor<int>(model.input2(), {7});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAre(false, true, false, false));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 1, 4));
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}
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TEST(ComparisonsTest, GreaterBroadcastTwoD) {
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ComparisonOpModel model({1, 1, 2, 4}, {1, 1, 1, 4}, TensorType_INT32,
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BuiltinOperator_GREATER);
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model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3, 2, 4, 2, 8});
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model.PopulateTensor<int>(model.input2(), {7, 1, 2, 4});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(),
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ElementsAre(false, true, true, false, false, true, false, true));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 2, 4));
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}
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TEST(ComparisonsTest, GreaterEqualFloat) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_FLOAT32,
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BuiltinOperator_GREATER_EQUAL);
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model.PopulateTensor<float>(model.input1(), {0.1, 0.9, 0.7, 0.3});
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model.PopulateTensor<float>(model.input2(), {0.1, 0.2, 0.6, 0.5});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAre(true, true, true, false));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 1, 4));
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}
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TEST(ComparisonsTest, GreaterEqualInt) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_INT32,
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BuiltinOperator_GREATER_EQUAL);
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model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3});
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model.PopulateTensor<int>(model.input2(), {1, 2, 7, 5});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAre(false, true, true, false));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 1, 4));
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}
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TEST(ComparisonsTest, GreaterEqualBroadcast) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 1}, TensorType_INT32,
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BuiltinOperator_GREATER_EQUAL);
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model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3});
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model.PopulateTensor<int>(model.input2(), {7});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAre(false, true, true, false));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 1, 4));
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}
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TEST(ComparisonsTest, GreaterEqualBroadcastTwoD) {
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ComparisonOpModel model({1, 1, 2, 4}, {1, 1, 1, 4}, TensorType_INT32,
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BuiltinOperator_GREATER_EQUAL);
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model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3, 2, 4, 2, 8});
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model.PopulateTensor<int>(model.input2(), {7, 1, 2, 4});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(),
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ElementsAre(false, true, true, false, false, true, true, true));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 2, 4));
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}
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TEST(ComparisonsTest, LessFloat) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_FLOAT32,
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BuiltinOperator_LESS);
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model.PopulateTensor<float>(model.input1(), {0.1, 0.9, 0.7, 0.3});
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model.PopulateTensor<float>(model.input2(), {0.1, 0.2, 0.6, 0.5});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAre(false, 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(ComparisonsTest, LessInt) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_INT32,
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BuiltinOperator_LESS);
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model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3});
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model.PopulateTensor<int>(model.input2(), {1, 2, 6, 5});
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model.Invoke();
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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(ComparisonsTest, LessBroadcast) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 1}, TensorType_INT32,
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BuiltinOperator_LESS);
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model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3});
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model.PopulateTensor<int>(model.input2(), {7});
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model.Invoke();
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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(ComparisonsTest, LessBroadcastTwoD) {
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ComparisonOpModel model({1, 1, 2, 4}, {1, 1, 1, 4}, TensorType_INT32,
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BuiltinOperator_LESS);
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model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3, 2, 4, 6, 8});
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model.PopulateTensor<int>(model.input2(), {7, 1, 2, 4});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(),
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ElementsAre(true, false, false, true, true, false, false, false));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 2, 4));
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}
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TEST(ComparisonsTest, LessEqualFloat) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_FLOAT32,
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BuiltinOperator_LESS_EQUAL);
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model.PopulateTensor<float>(model.input1(), {0.1, 0.9, 0.7, 0.3});
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model.PopulateTensor<float>(model.input2(), {0.1, 0.2, 0.6, 0.5});
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model.Invoke();
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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(ComparisonsTest, LessEqualInt) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, TensorType_INT32,
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BuiltinOperator_LESS_EQUAL);
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model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3});
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model.PopulateTensor<int>(model.input2(), {1, 2, 7, 5});
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model.Invoke();
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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(ComparisonsTest, LessEqualBroadcast) {
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ComparisonOpModel model({1, 1, 1, 4}, {1, 1, 1, 1}, TensorType_INT32,
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BuiltinOperator_LESS_EQUAL);
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model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3});
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model.PopulateTensor<int>(model.input2(), {7});
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model.Invoke();
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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(ComparisonsTest, LessEqualBroadcastTwoD) {
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ComparisonOpModel model({1, 1, 2, 4}, {1, 1, 1, 4}, TensorType_INT32,
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BuiltinOperator_LESS_EQUAL);
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model.PopulateTensor<int>(model.input1(), {-1, 9, 7, 3, 2, 4, 2, 8});
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model.PopulateTensor<int>(model.input2(), {7, 1, 2, 4});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(),
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ElementsAre(true, false, false, true, true, false, true, false));
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 2, 4));
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}
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TEST(QuantizedComparisonsTest, EqualUInt8Quantized) {
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const float kMin = -1.f;
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const float kMax = 128.f;
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ComparisonOpModel model({TensorType_UINT8, {1, 2, 2, 1}, kMin, kMax},
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{TensorType_UINT8, {1, 2, 2, 1}, kMin, kMax},
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TensorType_UINT8, BuiltinOperator_EQUAL);
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model.QuantizeAndPopulate<uint8_t>(model.input1(), {1, 9, 7, 3});
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model.QuantizeAndPopulate<uint8_t>(model.input2(), {1, 2, 7, 5});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAre(true, false, true, false));
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}
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TEST(QuantizedComparisonsTest, EqualInt8Quantized) {
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const float kMin = -127.f;
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const float kMax = 127.f;
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ComparisonOpModel model({TensorType_INT8, {1, 2, 2, 1}, kMin, kMax},
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{TensorType_INT8, {1, 2, 2, 1}, kMin, kMax},
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TensorType_INT8, BuiltinOperator_EQUAL);
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model.QuantizeAndPopulate<int8_t>(model.input1(), {1, -9, 7, 3});
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model.QuantizeAndPopulate<int8_t>(model.input2(), {-1, 2, 7, 5});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAre(false, false, true, false));
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}
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TEST(QuantizedComparisonsTest, NotEqualUInt8Quantized) {
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const float kMin = -1.f;
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const float kMax = 128.f;
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ComparisonOpModel model({TensorType_UINT8, {1, 2, 2, 1}, kMin, kMax},
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{TensorType_UINT8, {1, 2, 2, 1}, kMin, kMax},
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TensorType_UINT8, BuiltinOperator_NOT_EQUAL);
|
|
model.QuantizeAndPopulate<uint8_t>(model.input1(), {1, 9, 7, 3});
|
|
model.QuantizeAndPopulate<uint8_t>(model.input2(), {1, 2, 7, 0});
|
|
model.Invoke();
|
|
|
|
EXPECT_THAT(model.GetOutput(), ElementsAre(false, true, false, true));
|
|
}
|
|
|
|
TEST(QuantizedComparisonsTest, NotEqualInt8Quantized) {
|
|
const float kMin = -127.f;
|
|
const float kMax = 127.f;
|
|
ComparisonOpModel model({TensorType_INT8, {1, 2, 2, 1}, kMin, kMax},
|
|
{TensorType_INT8, {1, 2, 2, 1}, kMin, kMax},
|
|
TensorType_INT8, BuiltinOperator_NOT_EQUAL);
|
|
model.QuantizeAndPopulate<int8_t>(model.input1(), {1, -9, 7, 3});
|
|
model.QuantizeAndPopulate<int8_t>(model.input2(), {1, 2, 7, 5});
|
|
model.Invoke();
|
|
|
|
EXPECT_THAT(model.GetOutput(), ElementsAre(false, true, false, true));
|
|
}
|
|
|
|
TEST(ComparisonsTest, GreaterQuantized) {
|
|
const float kMin = -1.f;
|
|
const float kMax = 128.f;
|
|
ComparisonOpModel model({TensorType_UINT8, {1, 2, 2, 1}, kMin, kMax},
|
|
{TensorType_UINT8, {1, 2, 2, 1}, kMin, kMax},
|
|
TensorType_UINT8, BuiltinOperator_GREATER);
|
|
model.QuantizeAndPopulate<uint8_t>(model.input1(), {1, 9, 7, 3});
|
|
model.QuantizeAndPopulate<uint8_t>(model.input2(), {1, 2, 6, 5});
|
|
model.Invoke();
|
|
|
|
EXPECT_THAT(model.GetOutput(), ElementsAre(false, true, true, false));
|
|
}
|
|
|
|
TEST(ComparisonsTest, GreaterQuantizedSmallRange) {
|
|
ComparisonOpModel model({TensorType_UINT8, {1, 2, 2, 1}, 0.0, 1.0},
|
|
{TensorType_UINT8, {1, 2, 2, 1}, 0.0, 2.0},
|
|
TensorType_UINT8, BuiltinOperator_GREATER);
|
|
model.QuantizeAndPopulate<uint8_t>(model.input1(), {1.0, 0.5, 0.35, 0.1});
|
|
model.QuantizeAndPopulate<uint8_t>(model.input2(), {1.01, 0.25, 0.3, 0.4});
|
|
model.Invoke();
|
|
|
|
EXPECT_THAT(model.GetOutput(), ElementsAre(false, true, true, false));
|
|
}
|
|
|
|
TEST(ComparisonsTest, GreaterEqualQuantized) {
|
|
const float kMin = -1.f;
|
|
const float kMax = 128.f;
|
|
ComparisonOpModel model({TensorType_UINT8, {1, 2, 2, 1}, kMin, kMax},
|
|
{TensorType_UINT8, {1, 2, 2, 1}, kMin, kMax},
|
|
TensorType_UINT8, BuiltinOperator_GREATER_EQUAL);
|
|
model.QuantizeAndPopulate<uint8_t>(model.input1(), {1, 9, 7, 3});
|
|
model.QuantizeAndPopulate<uint8_t>(model.input2(), {1, 2, 6, 5});
|
|
model.Invoke();
|
|
|
|
EXPECT_THAT(model.GetOutput(), ElementsAre(true, true, true, false));
|
|
}
|
|
|
|
TEST(ComparisonsTest, LessQuantized) {
|
|
const float kMin = -1.f;
|
|
const float kMax = 128.f;
|
|
ComparisonOpModel model({TensorType_UINT8, {1, 2, 2, 1}, kMin, kMax},
|
|
{TensorType_UINT8, {1, 2, 2, 1}, kMin, kMax},
|
|
TensorType_UINT8, BuiltinOperator_LESS);
|
|
model.QuantizeAndPopulate<uint8_t>(model.input1(), {1, 9, 7, 3});
|
|
model.QuantizeAndPopulate<uint8_t>(model.input2(), {1, 2, 6, 5});
|
|
model.Invoke();
|
|
|
|
EXPECT_THAT(model.GetOutput(), ElementsAre(false, false, false, true));
|
|
}
|
|
|
|
TEST(ComparisonsTest, LessEqualQuantized) {
|
|
const float kMin = -1.f;
|
|
const float kMax = 128.f;
|
|
ComparisonOpModel model({TensorType_UINT8, {1, 2, 2, 1}, kMin, kMax},
|
|
{TensorType_UINT8, {1, 2, 2, 1}, kMin, kMax},
|
|
TensorType_UINT8, BuiltinOperator_LESS_EQUAL);
|
|
model.QuantizeAndPopulate<uint8_t>(model.input1(), {1, 9, 7, 3});
|
|
model.QuantizeAndPopulate<uint8_t>(model.input2(), {1, 2, 6, 5});
|
|
model.Invoke();
|
|
|
|
EXPECT_THAT(model.GetOutput(), ElementsAre(true, false, false, true));
|
|
}
|
|
|
|
TEST(ComparisonsTest, QuantizedEqualWithBroadcast) {
|
|
const float kMin = -1.f;
|
|
const float kMax = 128.f;
|
|
std::vector<std::vector<int>> test_shapes = {
|
|
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
|
|
for (int i = 0; i < test_shapes.size(); ++i) {
|
|
ComparisonOpModel model({TensorType_UINT8, test_shapes[i], kMin, kMax},
|
|
{TensorType_UINT8, {}, kMin, kMax},
|
|
TensorType_UINT8, BuiltinOperator_EQUAL);
|
|
model.QuantizeAndPopulate<uint8_t>(model.input1(), {20, 2, 7, 8, 11, 20});
|
|
model.QuantizeAndPopulate<uint8_t>(model.input2(), {2});
|
|
model.Invoke();
|
|
EXPECT_THAT(model.GetOutput(),
|
|
ElementsAre(false, true, false, false, false, false))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
TEST(ComparisonsTest, QuantizedUInt8NotEqualWithBroadcast) {
|
|
const float kMin = -1.f;
|
|
const float kMax = 128.f;
|
|
std::vector<std::vector<int>> test_shapes = {
|
|
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
|
|
for (int i = 0; i < test_shapes.size(); ++i) {
|
|
ComparisonOpModel model({TensorType_UINT8, test_shapes[i], kMin, kMax},
|
|
{TensorType_UINT8, {}, kMin, kMax},
|
|
TensorType_UINT8, BuiltinOperator_NOT_EQUAL);
|
|
model.QuantizeAndPopulate<uint8_t>(model.input1(), {20, 2, 7, 8, 11, 20});
|
|
model.QuantizeAndPopulate<uint8_t>(model.input2(), {2});
|
|
model.Invoke();
|
|
EXPECT_THAT(model.GetOutput(),
|
|
ElementsAre(true, false, true, true, true, true))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
TEST(ComparisonsTest, QuantizedInt8NotEqualWithBroadcast) {
|
|
const float kMin = -127.f;
|
|
const float kMax = 127.f;
|
|
std::vector<std::vector<int>> test_shapes = {
|
|
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
|
|
for (int i = 0; i < test_shapes.size(); ++i) {
|
|
ComparisonOpModel model({TensorType_INT8, test_shapes[i], kMin, kMax},
|
|
{TensorType_INT8, {}, kMin, kMax}, TensorType_INT8,
|
|
BuiltinOperator_NOT_EQUAL);
|
|
model.QuantizeAndPopulate<int8_t>(model.input1(), {-20, 2, 7, -8, 11, 20});
|
|
model.QuantizeAndPopulate<int8_t>(model.input2(), {2});
|
|
model.Invoke();
|
|
EXPECT_THAT(model.GetOutput(),
|
|
ElementsAre(true, false, true, true, true, true))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
TEST(ComparisonsTest, QuantizedUInt8GreaterWithBroadcast) {
|
|
const float kMin = -1.f;
|
|
const float kMax = 128.f;
|
|
std::vector<std::vector<int>> test_shapes = {
|
|
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
|
|
for (int i = 0; i < test_shapes.size(); ++i) {
|
|
ComparisonOpModel model({TensorType_UINT8, test_shapes[i], kMin, kMax},
|
|
{TensorType_UINT8, {}, kMin, kMax},
|
|
TensorType_UINT8, BuiltinOperator_GREATER);
|
|
model.QuantizeAndPopulate<uint8_t>(model.input1(), {20, 2, 7, 8, 11, 20});
|
|
model.QuantizeAndPopulate<uint8_t>(model.input2(), {8});
|
|
model.Invoke();
|
|
EXPECT_THAT(model.GetOutput(),
|
|
ElementsAre(true, false, false, false, true, true))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
TEST(ComparisonsTest, QuantizedInt8GreaterWithBroadcast) {
|
|
const float kMin = -127.f;
|
|
const float kMax = 127.f;
|
|
std::vector<std::vector<int>> test_shapes = {
|
|
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
|
|
for (int i = 0; i < test_shapes.size(); ++i) {
|
|
ComparisonOpModel model({TensorType_INT8, test_shapes[i], kMin, kMax},
|
|
{TensorType_INT8, {}, kMin, kMax}, TensorType_INT8,
|
|
BuiltinOperator_GREATER);
|
|
model.QuantizeAndPopulate<int8_t>(model.input1(), {20, -2, -71, 8, 11, 20});
|
|
model.QuantizeAndPopulate<int8_t>(model.input2(), {8});
|
|
model.Invoke();
|
|
EXPECT_THAT(model.GetOutput(),
|
|
ElementsAre(true, false, false, false, true, true))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
TEST(ComparisonsTest, QuantizedUInt8GreaterEqualWithBroadcast) {
|
|
const float kMin = -1.f;
|
|
const float kMax = 128.f;
|
|
std::vector<std::vector<int>> test_shapes = {
|
|
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
|
|
for (int i = 0; i < test_shapes.size(); ++i) {
|
|
ComparisonOpModel model({TensorType_UINT8, test_shapes[i], kMin, kMax},
|
|
{TensorType_UINT8, {}, kMin, kMax},
|
|
TensorType_UINT8, BuiltinOperator_GREATER_EQUAL);
|
|
model.QuantizeAndPopulate<uint8_t>(model.input1(), {20, 2, 7, 8, 11, 20});
|
|
model.QuantizeAndPopulate<uint8_t>(model.input2(), {8});
|
|
model.Invoke();
|
|
EXPECT_THAT(model.GetOutput(),
|
|
ElementsAre(true, false, false, true, true, true))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
TEST(ComparisonsTest, QuantizedInt8GreaterEqualWithBroadcast) {
|
|
const float kMin = -127.f;
|
|
const float kMax = 127.f;
|
|
std::vector<std::vector<int>> test_shapes = {
|
|
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
|
|
for (int i = 0; i < test_shapes.size(); ++i) {
|
|
ComparisonOpModel model({TensorType_INT8, test_shapes[i], kMin, kMax},
|
|
{TensorType_INT8, {}, kMin, kMax}, TensorType_INT8,
|
|
BuiltinOperator_GREATER_EQUAL);
|
|
model.QuantizeAndPopulate<int8_t>(model.input1(), {20, -2, -71, 8, 11, 20});
|
|
model.QuantizeAndPopulate<int8_t>(model.input2(), {8});
|
|
model.Invoke();
|
|
EXPECT_THAT(model.GetOutput(),
|
|
ElementsAre(true, false, false, true, true, true))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
TEST(ComparisonsTest, QuantizedUInt8LessWithBroadcast) {
|
|
const float kMin = -1.f;
|
|
const float kMax = 128.f;
|
|
std::vector<std::vector<int>> test_shapes = {
|
|
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
|
|
for (int i = 0; i < test_shapes.size(); ++i) {
|
|
ComparisonOpModel model({TensorType_UINT8, test_shapes[i], kMin, kMax},
|
|
{TensorType_UINT8, {}, kMin, kMax},
|
|
TensorType_UINT8, BuiltinOperator_LESS);
|
|
model.QuantizeAndPopulate<uint8_t>(model.input1(), {20, 2, 7, 8, 11, 20});
|
|
model.QuantizeAndPopulate<uint8_t>(model.input2(), {8});
|
|
model.Invoke();
|
|
EXPECT_THAT(model.GetOutput(),
|
|
ElementsAre(false, true, true, false, false, false))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
TEST(ComparisonsTest, QuantizedInt8LessWithBroadcast) {
|
|
const float kMin = -127.f;
|
|
const float kMax = 127.f;
|
|
std::vector<std::vector<int>> test_shapes = {
|
|
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
|
|
for (int i = 0; i < test_shapes.size(); ++i) {
|
|
ComparisonOpModel model({TensorType_INT8, test_shapes[i], kMin, kMax},
|
|
{TensorType_INT8, {}, kMin, kMax}, TensorType_INT8,
|
|
BuiltinOperator_LESS);
|
|
model.QuantizeAndPopulate<int8_t>(model.input1(), {20, -2, -71, 8, 11, 20});
|
|
model.QuantizeAndPopulate<int8_t>(model.input2(), {8});
|
|
model.Invoke();
|
|
EXPECT_THAT(model.GetOutput(),
|
|
ElementsAre(false, true, true, false, false, false))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
TEST(ComparisonsTest, QuantizedUInt8LessEqualWithBroadcast) {
|
|
const float kMin = -1.f;
|
|
const float kMax = 128.f;
|
|
std::vector<std::vector<int>> test_shapes = {
|
|
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
|
|
for (int i = 0; i < test_shapes.size(); ++i) {
|
|
ComparisonOpModel model({TensorType_UINT8, test_shapes[i], kMin, kMax},
|
|
{TensorType_UINT8, {}, kMin, kMax},
|
|
TensorType_UINT8, BuiltinOperator_LESS_EQUAL);
|
|
model.QuantizeAndPopulate<uint8_t>(model.input1(), {20, 2, 7, 8, 11, 20});
|
|
model.QuantizeAndPopulate<uint8_t>(model.input2(), {8});
|
|
model.Invoke();
|
|
EXPECT_THAT(model.GetOutput(),
|
|
ElementsAre(false, true, true, true, false, false))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
TEST(ComparisonsTest, QuantizedInt8LessEqualWithBroadcast) {
|
|
const float kMin = -127.f;
|
|
const float kMax = 127.f;
|
|
std::vector<std::vector<int>> test_shapes = {
|
|
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
|
|
for (int i = 0; i < test_shapes.size(); ++i) {
|
|
ComparisonOpModel model({TensorType_INT8, test_shapes[i], kMin, kMax},
|
|
{TensorType_INT8, {}, kMin, kMax}, TensorType_INT8,
|
|
BuiltinOperator_LESS_EQUAL);
|
|
model.QuantizeAndPopulate<int8_t>(model.input1(), {20, -2, -71, 8, 11, 20});
|
|
model.QuantizeAndPopulate<int8_t>(model.input2(), {8});
|
|
model.Invoke();
|
|
EXPECT_THAT(model.GetOutput(),
|
|
ElementsAre(false, true, true, true, false, false))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
} // namespace
|
|
} // namespace tflite
|