wehub-resource-sync
187 lines
7.1 KiB
C++
187 lines
7.1 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 <string>
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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::ElementsAreArray;
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using ::testing::IsEmpty;
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class BaseSqueezeOpModel : public SingleOpModel {
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public:
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BaseSqueezeOpModel(const TensorData& input, const TensorData& output,
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std::initializer_list<int> axis) {
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input_ = AddInput(input);
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output_ = AddOutput(output);
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SetBuiltinOp(
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BuiltinOperator_SQUEEZE, BuiltinOptions_SqueezeOptions,
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CreateSqueezeOptions(builder_, builder_.CreateVector<int>(axis))
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.Union());
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BuildInterpreter({GetShape(input_)});
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}
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int input() { return input_; }
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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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template <typename T>
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class SqueezeOpModel : public BaseSqueezeOpModel {
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public:
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using BaseSqueezeOpModel::BaseSqueezeOpModel;
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void SetInput(std::initializer_list<T> data) { PopulateTensor(input_, data); }
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void SetStringInput(std::initializer_list<string> data) {
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PopulateStringTensor(input_, data);
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}
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std::vector<T> GetOutput() { return ExtractVector<T>(output_); }
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std::vector<string> GetStringOutput() {
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return ExtractVector<string>(output_);
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}
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std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
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};
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template <typename T>
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class SqueezeOpTest : public ::testing::Test {};
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using DataTypes = ::testing::Types<float, int8_t, int16_t, int32_t>;
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TYPED_TEST_SUITE(SqueezeOpTest, DataTypes);
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TYPED_TEST(SqueezeOpTest, SqueezeAllInplace) {
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std::initializer_list<TypeParam> data = {1, 2, 3, 4, 5, 6, 7, 8,
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9, 10, 11, 12, 13, 14, 15, 16,
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17, 18, 19, 20, 21, 22, 23, 24};
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SqueezeOpModel<TypeParam> m({GetTensorType<TypeParam>(), {1, 24, 1}},
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{GetTensorType<TypeParam>(), {24}}, {});
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m.SetInput(data);
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const int kInplaceInputTensorIdx = 0;
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const int kInplaceOutputTensorIdx = 0;
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const TfLiteTensor* input_tensor = m.GetInputTensor(kInplaceInputTensorIdx);
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TfLiteTensor* output_tensor = m.GetOutputTensor(kInplaceOutputTensorIdx);
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output_tensor->data.data = input_tensor->data.data;
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({24}));
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EXPECT_THAT(
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m.GetOutput(),
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ElementsAreArray({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}));
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EXPECT_EQ(output_tensor->data.data, input_tensor->data.data);
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}
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TYPED_TEST(SqueezeOpTest, SqueezeAll) {
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std::initializer_list<TypeParam> data = {1, 2, 3, 4, 5, 6, 7, 8,
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9, 10, 11, 12, 13, 14, 15, 16,
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17, 18, 19, 20, 21, 22, 23, 24};
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SqueezeOpModel<TypeParam> m({GetTensorType<TypeParam>(), {1, 24, 1}},
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{GetTensorType<TypeParam>(), {24}}, {});
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m.SetInput(data);
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({24}));
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EXPECT_THAT(
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m.GetOutput(),
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ElementsAreArray({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}));
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}
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TYPED_TEST(SqueezeOpTest, SqueezeSelectedAxis) {
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std::initializer_list<TypeParam> data = {1, 2, 3, 4, 5, 6, 7, 8,
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9, 10, 11, 12, 13, 14, 15, 16,
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17, 18, 19, 20, 21, 22, 23, 24};
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SqueezeOpModel<TypeParam> m({GetTensorType<TypeParam>(), {1, 24, 1}},
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{GetTensorType<TypeParam>(), {24}}, {2});
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m.SetInput(data);
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 24}));
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EXPECT_THAT(
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m.GetOutput(),
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ElementsAreArray({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}));
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}
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TYPED_TEST(SqueezeOpTest, SqueezeNegativeAxis) {
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std::initializer_list<TypeParam> data = {1, 2, 3, 4, 5, 6, 7, 8,
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9, 10, 11, 12, 13, 14, 15, 16,
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17, 18, 19, 20, 21, 22, 23, 24};
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SqueezeOpModel<TypeParam> m({GetTensorType<TypeParam>(), {1, 24, 1}},
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{GetTensorType<TypeParam>(), {24}}, {-1, 0});
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m.SetInput(data);
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({24}));
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EXPECT_THAT(
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m.GetOutput(),
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ElementsAreArray({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}));
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}
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TYPED_TEST(SqueezeOpTest, SqueezeAllDims) {
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std::initializer_list<TypeParam> data = {3};
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SqueezeOpModel<TypeParam> m(
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{GetTensorType<TypeParam>(), {1, 1, 1, 1, 1, 1, 1}},
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{GetTensorType<TypeParam>(), {1}}, {});
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m.SetInput(data);
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutputShape(), IsEmpty());
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({3}));
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}
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TEST(SqueezeOpTest, SqueezeAllString) {
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std::initializer_list<std::string> data = {"a", "b"};
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SqueezeOpModel<std::string> m({GetTensorType<std::string>(), {1, 2, 1}},
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{GetTensorType<std::string>(), {2}}, {});
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m.SetStringInput(data);
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
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EXPECT_THAT(m.GetStringOutput(), ElementsAreArray({"a", "b"}));
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}
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TEST(SqueezeOpTest, SqueezeNegativeAxisString) {
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std::initializer_list<std::string> data = {"a", "b"};
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SqueezeOpModel<std::string> m({GetTensorType<std::string>(), {1, 2, 1}},
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{GetTensorType<std::string>(), {24}}, {-1});
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m.SetStringInput(data);
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2}));
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EXPECT_THAT(m.GetStringOutput(), ElementsAreArray({"a", "b"}));
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}
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TEST(SqueezeOpTest, SqueezeAllDimsString) {
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std::initializer_list<std::string> data = {"a"};
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SqueezeOpModel<std::string> m(
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{GetTensorType<std::string>(), {1, 1, 1, 1, 1, 1, 1}},
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{GetTensorType<std::string>(), {1}}, {});
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m.SetStringInput(data);
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutputShape(), IsEmpty());
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EXPECT_THAT(m.GetStringOutput(), ElementsAreArray({"a"}));
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}
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} // namespace
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} // namespace tflite
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