wehub-resource-sync
97 lines
3.0 KiB
C++
97 lines
3.0 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 <stdint.h>
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#include <initializer_list>
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#include <memory>
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#include <vector>
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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/core/interpreter.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::ElementsAreArray;
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class RankOpModel : public SingleOpModel {
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public:
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RankOpModel(std::initializer_list<int> input_shape, TensorType input_type) {
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TensorType output_type = TensorType_INT32;
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input_ = AddInput(input_type);
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output_ = AddOutput(output_type);
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SetBuiltinOp(BuiltinOperator_RANK, BuiltinOptions_RankOptions,
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CreateRankOptions(builder_).Union());
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BuildInterpreter({input_shape});
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}
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TfLiteStatus InvokeWithResult() { return interpreter_->Invoke(); }
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int input() { return input_; }
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std::vector<int32_t> GetOutput() { return ExtractVector<int32_t>(output_); }
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std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
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TfLiteAllocationType GetOutputAllocationType() const {
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return interpreter_->tensor(interpreter_->outputs()[0])->allocation_type;
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}
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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(RankOpTest, InputTypeFloat) {
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RankOpModel model({1, 3, 1, 3, 5}, TensorType_FLOAT32);
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ASSERT_EQ(model.GetOutputAllocationType(), kTfLitePersistentRo);
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// Unlike most ops, Rank populates outputs in Prepare().
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({5}));
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EXPECT_TRUE(model.GetOutputShape().empty());
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// Invoke is superfluous and shouldn't change the output.
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({5}));
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EXPECT_TRUE(model.GetOutputShape().empty());
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}
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TEST(RankOpTest, InputTypeInt) {
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RankOpModel model({1, 3, 1, 3, 5}, TensorType_INT32);
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({5}));
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EXPECT_TRUE(model.GetOutputShape().empty());
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}
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TEST(RankOpTest, ScalarTensor) {
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RankOpModel model({}, TensorType_FLOAT32);
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({0}));
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EXPECT_TRUE(model.GetOutputShape().empty());
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}
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TEST(RankOpTest, EmptyTensor) {
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RankOpModel model({1, 0}, TensorType_FLOAT32);
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({2}));
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EXPECT_TRUE(model.GetOutputShape().empty());
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}
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} // namespace
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} // namespace tflite
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