wehub-resource-sync
198 lines
7.7 KiB
C++
198 lines
7.7 KiB
C++
/* Copyright 2021 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 "tensorflow/lite/kernels/custom_ops_register.h"
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#include "tensorflow/lite/kernels/internal/types.h"
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#include "tensorflow/lite/kernels/test_util.h"
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namespace tflite {
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using ::testing::ElementsAreArray;
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class BaseRollOpModel : public SingleOpModel {
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public:
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BaseRollOpModel(TensorData input, const std::vector<int32_t>& shift,
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const std::vector<int64_t>& axis, TensorData output) {
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if (input.type == TensorType_FLOAT32 || input.type == TensorType_INT64) {
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// Clear quantization params.
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input.min = input.max = 0.f;
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output.min = output.max = 0.f;
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}
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input_ = AddInput(input);
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shift_ = AddInput(
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TensorData(TensorType_INT32, {static_cast<int>(shift.size())}));
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axis_ =
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AddInput(TensorData(TensorType_INT64, {static_cast<int>(axis.size())}));
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output_ = AddOutput(output);
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SetCustomOp("Roll", {}, ops::custom::Register_ROLL);
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BuildInterpreter({GetShape(input_), GetShape(shift_), GetShape(axis_)});
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PopulateTensor(shift_, shift);
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PopulateTensor(axis_, axis);
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}
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template <typename T>
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inline typename std::enable_if<is_small_integer<T>::value, void>::type
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SetInput(const std::initializer_list<float>& data) {
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QuantizeAndPopulate<T>(input_, data);
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}
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template <typename T>
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inline typename std::enable_if<!is_small_integer<T>::value, void>::type
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SetInput(std::initializer_list<T> data) {
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PopulateTensor(input_, data);
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}
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template <typename T>
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inline typename std::enable_if<is_small_integer<T>::value,
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std::vector<float>>::type
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GetOutput() {
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return Dequantize<T>(ExtractVector<T>(output_), GetScale(output_),
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GetZeroPoint(output_));
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}
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template <typename T>
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inline
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typename std::enable_if<!is_small_integer<T>::value, std::vector<T>>::type
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GetOutput() {
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return ExtractVector<T>(output_);
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}
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void SetStringInput(std::initializer_list<std::string> data) {
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PopulateStringTensor(input_, data);
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}
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protected:
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int input_;
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int shift_;
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int axis_;
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int output_;
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};
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#if GTEST_HAS_DEATH_TEST
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TEST(RollOpTest, MismatchSize) {
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EXPECT_DEATH(BaseRollOpModel m(/*input=*/{TensorType_FLOAT32, {1, 2, 4, 2}},
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/*shift=*/{2, 3}, /*axis=*/{2},
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/*output=*/{TensorType_FLOAT32, {}}),
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"NumElements.shift. != NumElements.axis.");
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}
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#endif
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template <typename T>
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class RollOpTest : public ::testing::Test {};
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using DataTypes = ::testing::Types<float, int8_t, int16_t, int64_t>;
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TYPED_TEST_SUITE(RollOpTest, DataTypes);
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TYPED_TEST(RollOpTest, Roll1D) {
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BaseRollOpModel m(
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/*input=*/{GetTensorType<TypeParam>(), {10}, 0, 31.875},
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/*shift=*/{3}, /*axis=*/{0},
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/*output=*/{GetTensorType<TypeParam>(), {}, 0, 31.875});
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m.SetInput<TypeParam>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutput<TypeParam>(),
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ElementsAreArray({7, 8, 9, 0, 1, 2, 3, 4, 5, 6}));
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}
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TYPED_TEST(RollOpTest, Roll3D) {
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BaseRollOpModel m(
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/*input=*/{GetTensorType<TypeParam>(), {2, 4, 4}, 0, 31.875},
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/*shift=*/{2, 6}, /*axis=*/{1, 2},
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/*output=*/{GetTensorType<TypeParam>(), {}, 0, 31.875});
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m.SetInput<TypeParam>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
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11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
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22, 23, 24, 25, 26, 27, 28, 29, 30, 31});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutput<TypeParam>(),
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ElementsAreArray({10, 11, 8, 9, 14, 15, 12, 13, 2, 3, 0,
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1, 6, 7, 4, 5, 26, 27, 24, 25, 30, 31,
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28, 29, 18, 19, 16, 17, 22, 23, 20, 21}));
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}
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TYPED_TEST(RollOpTest, Roll3DNegativeShift) {
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BaseRollOpModel m(
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/*input=*/{GetTensorType<TypeParam>(), {2, 4, 4}, 0, 31.875},
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/*shift=*/{2, -5}, /*axis=*/{1, -1},
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/*output=*/{GetTensorType<TypeParam>(), {}, 0, 31.875});
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m.SetInput<TypeParam>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
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11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
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22, 23, 24, 25, 26, 27, 28, 29, 30, 31});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutput<TypeParam>(),
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ElementsAreArray({9, 10, 11, 8, 13, 14, 15, 12, 1, 2, 3,
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0, 5, 6, 7, 4, 25, 26, 27, 24, 29, 30,
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31, 28, 17, 18, 19, 16, 21, 22, 23, 20}));
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}
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TYPED_TEST(RollOpTest, DuplicatedAxis) {
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BaseRollOpModel m(
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/*input=*/{GetTensorType<TypeParam>(), {2, 4, 4}, 0, 31.875},
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/*shift=*/{2, 3}, /*axis=*/{1, 1},
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/*output=*/{GetTensorType<TypeParam>(), {}, 0, 31.875});
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m.SetInput<TypeParam>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
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11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
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22, 23, 24, 25, 26, 27, 28, 29, 30, 31});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutput<TypeParam>(),
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ElementsAreArray({12, 13, 14, 15, 0, 1, 2, 3, 4, 5, 6,
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7, 8, 9, 10, 11, 28, 29, 30, 31, 16, 17,
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18, 19, 20, 21, 22, 23, 24, 25, 26, 27}));
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}
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TEST(RollOpTest, Roll3DTring) {
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BaseRollOpModel m(/*input=*/{TensorType_STRING, {2, 4, 4}},
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/*shift=*/{2, 5}, /*axis=*/{1, 2},
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/*output=*/{TensorType_STRING, {}});
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m.SetStringInput({"0", "1", "2", "3", "4", "5", "6", "7",
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"8", "9", "10", "11", "12", "13", "14", "15",
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"16", "17", "18", "19", "20", "21", "22", "23",
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"24", "25", "26", "27", "28", "29", "30", "31"});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(
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m.GetOutput<std::string>(),
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ElementsAreArray({"11", "8", "9", "10", "15", "12", "13", "14",
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"3", "0", "1", "2", "7", "4", "5", "6",
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"27", "24", "25", "26", "31", "28", "29", "30",
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"19", "16", "17", "18", "23", "20", "21", "22"}));
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}
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TEST(RollOpTest, BoolRoll3D) {
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BaseRollOpModel m(/*input=*/{TensorType_BOOL, {2, 4, 4}},
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/*shift=*/{2, 3}, /*axis=*/{1, 2},
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/*output=*/{TensorType_BOOL, {}});
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m.SetInput<bool>({true, false, false, true, true, false, false, true,
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false, false, false, true, false, false, true, true,
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false, false, true, false, false, false, true, false,
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false, true, true, false, false, true, false, false});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutput<bool>(),
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ElementsAreArray({false, false, true, false, false, true, true,
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false, false, false, true, true, false, false,
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true, true, true, true, false, false, true,
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false, false, false, false, true, false, false,
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false, true, false, false}));
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}
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} // namespace tflite
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