wehub-resource-sync
93 lines
3.3 KiB
C++
93 lines
3.3 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 <stdint.h>
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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/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 ZerosLikeOpModel : public SingleOpModel {
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public:
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explicit ZerosLikeOpModel(const TensorData& input) {
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input_ = AddInput(input);
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output_ = AddOutput(input);
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SetBuiltinOp(BuiltinOperator_ZEROS_LIKE, BuiltinOptions_ZerosLikeOptions,
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CreateZerosLikeOptions(builder_).Union());
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BuildInterpreter({GetShape(input_)});
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}
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int input() { return input_; }
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int output() { return output_; }
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protected:
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int input_;
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int output_;
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};
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TEST(ZerosLikeOpModel, ZerosLikeFloat) {
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ZerosLikeOpModel m({TensorType_FLOAT32, {2, 3}});
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m.PopulateTensor<float>(m.input(), {-2.0, -1.0, 0.0, 1.0, 2.0, 3.0});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.ExtractVector<float>(m.output()),
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Pointwise(FloatingPointEq(), {0.0, 0.0, 0.0, 0.0, 0.0, 0.0}));
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EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({2, 3}));
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}
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TEST(ZerosLikeOpModel, ZerosLikeInt32) {
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ZerosLikeOpModel m({TensorType_INT32, {1, 2, 2, 1}});
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m.PopulateTensor<int32_t>(m.input(), {-2, -1, 0, 3});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.ExtractVector<int32_t>(m.output()),
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ElementsAreArray({0, 0, 0, 0}));
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EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 2, 2, 1}));
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}
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TEST(ZerosLikeOpModel, ZerosLikeInt64) {
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ZerosLikeOpModel m({TensorType_INT64, {1, 2, 2, 1}});
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m.PopulateTensor<int64_t>(m.input(), {-2, -1, 0, 3});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.ExtractVector<int64_t>(m.output()),
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ElementsAreArray({0, 0, 0, 0}));
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EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 2, 2, 1}));
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}
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TEST(ZerosLikeOpModel, InvalidTypeTest) {
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ZerosLikeOpModel m_uint8({TensorType_UINT8, {1, 1}});
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ASSERT_NE(m_uint8.Invoke(), kTfLiteOk)
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<< "ZerosLike only currently supports int64, int32, and float32";
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ZerosLikeOpModel m_int16({TensorType_INT16, {1, 1}});
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ASSERT_NE(m_int16.Invoke(), kTfLiteOk)
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<< "ZerosLike only currently supports int64, int32, and float32";
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ZerosLikeOpModel m_complex({TensorType_COMPLEX64, {1, 1}});
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ASSERT_NE(m_complex.Invoke(), kTfLiteOk)
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<< "ZerosLike only currently supports int64, int32, and float32";
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ZerosLikeOpModel m_int8({TensorType_INT8, {1, 1}});
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ASSERT_NE(m_int8.Invoke(), kTfLiteOk)
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<< "ZerosLike only currently supports int64, int32, and float32";
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}
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} // namespace
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} // namespace tflite
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