127 lines
4.2 KiB
C++
127 lines
4.2 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 <stdint.h>
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#include <vector>
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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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namespace tflite {
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namespace {
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using ::testing::ElementsAre;
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template <typename T>
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class RangeOpModel : public SingleOpModel {
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public:
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explicit RangeOpModel(const TensorType& dtype) {
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start_ = AddInput(dtype);
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limit_ = AddInput(dtype);
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delta_ = AddInput(dtype);
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output_ = AddOutput(dtype);
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SetBuiltinOp(BuiltinOperator_RANGE, BuiltinOptions_RangeOptions,
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CreateRangeOptions(builder_).Union());
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BuildInterpreter({GetShape(start_), GetShape(limit_), GetShape(delta_)});
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}
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int start() { return start_; }
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int limit() { return limit_; }
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int delta() { return delta_; }
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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 start_;
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int limit_;
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int delta_;
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int output_;
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};
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TEST(RangeOpModel, Simple) {
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RangeOpModel<int32_t> model(TensorType_INT32);
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model.PopulateTensor<int32_t>(model.start(), {0});
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model.PopulateTensor<int32_t>(model.limit(), {4});
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model.PopulateTensor<int32_t>(model.delta(), {1});
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model.Invoke();
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4));
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EXPECT_THAT(model.GetOutput(), ElementsAre(0, 1, 2, 3));
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}
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TEST(RangeOpModel, DeltaGreaterThanOne) {
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RangeOpModel<int32_t> model(TensorType_INT32);
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model.PopulateTensor<int32_t>(model.start(), {2});
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model.PopulateTensor<int32_t>(model.limit(), {9});
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model.PopulateTensor<int32_t>(model.delta(), {2});
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model.Invoke();
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4));
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EXPECT_THAT(model.GetOutput(), ElementsAre(2, 4, 6, 8));
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}
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TEST(RangeOpModel, NegativeDelta) {
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RangeOpModel<int32_t> model(TensorType_INT32);
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model.PopulateTensor<int32_t>(model.start(), {10});
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model.PopulateTensor<int32_t>(model.limit(), {3});
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model.PopulateTensor<int32_t>(model.delta(), {-3});
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model.Invoke();
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(3));
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EXPECT_THAT(model.GetOutput(), ElementsAre(10, 7, 4));
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}
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TEST(RangeOpModel, FloatSimple) {
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RangeOpModel<float> model(TensorType_FLOAT32);
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model.PopulateTensor<float>(model.start(), {0});
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model.PopulateTensor<float>(model.limit(), {4});
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model.PopulateTensor<float>(model.delta(), {1});
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model.Invoke();
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4));
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EXPECT_THAT(model.GetOutput(), ElementsAre(0, 1, 2, 3));
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}
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TEST(RangeOpModel, FloatDeltaGreaterThanOne) {
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RangeOpModel<float> model(TensorType_FLOAT32);
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model.PopulateTensor<float>(model.start(), {2});
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model.PopulateTensor<float>(model.limit(), {9});
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model.PopulateTensor<float>(model.delta(), {2});
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model.Invoke();
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4));
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EXPECT_THAT(model.GetOutput(), ElementsAre(2, 4, 6, 8));
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}
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TEST(RangeOpModel, FloatNegativeDelta) {
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RangeOpModel<float> model(TensorType_FLOAT32);
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model.PopulateTensor<float>(model.start(), {10});
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model.PopulateTensor<float>(model.limit(), {3});
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model.PopulateTensor<float>(model.delta(), {-3});
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model.Invoke();
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(3));
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EXPECT_THAT(model.GetOutput(), ElementsAre(10, 7, 4));
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}
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TEST(RangeOpModel, EmptyOutput) {
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RangeOpModel<int32_t> model(TensorType_INT32);
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model.PopulateTensor<int32_t>(model.start(), {0});
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model.PopulateTensor<int32_t>(model.limit(), {0});
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model.PopulateTensor<int32_t>(model.delta(), {1});
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model.Invoke();
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(0));
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EXPECT_THAT(model.GetOutput(), ElementsAre());
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}
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} // namespace
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} // namespace tflite
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