204 lines
8.3 KiB
C++
204 lines
8.3 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 <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/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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template <typename T>
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class BaseMirrorPadOpModel : public SingleOpModel {
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public:
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BaseMirrorPadOpModel(const TensorData& input,
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const TensorData& padding_matrix,
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const TensorData& output,
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const tflite::MirrorPadMode mode) {
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input_id_ = AddInput(input);
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padding_matrix_id_ = AddInput(padding_matrix);
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output_id_ = AddOutput(output);
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SetBuiltinOp(BuiltinOperator_MIRROR_PAD, BuiltinOptions_MirrorPadOptions,
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CreateMirrorPadOptions(builder_, mode).Union());
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BuildInterpreter({GetShape(input_id_), GetShape(padding_matrix_id_)});
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}
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int input_tensor_id() { return input_id_; }
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int padding_matrix_tensor_id() { return padding_matrix_id_; }
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std::vector<T> GetOutput() { return ExtractVector<T>(output_id_); }
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protected:
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int input_id_;
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int padding_matrix_id_;
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int output_id_;
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};
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TEST(MirrorPadTest, EmptyPad) {
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BaseMirrorPadOpModel<int> model(
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{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
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{TensorType_INT32, {}}, tflite::MirrorPadMode_REFLECT);
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model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
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model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {0, 0, 0, 0});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6}));
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}
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TEST(MirrorPadTest, PadOneSide_right_Reflect) {
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BaseMirrorPadOpModel<int> model(
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{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
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{TensorType_INT32, {}}, tflite::MirrorPadMode_REFLECT);
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model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
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model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {0, 1, 0, 1});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({1, 2, 3, 2, 4, 5, 6, 5, 1, 2, 3, 2}));
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}
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TEST(MirrorPadTest, PadOneSide_left_Reflect) {
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BaseMirrorPadOpModel<int> model(
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{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
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{TensorType_INT32, {}}, tflite::MirrorPadMode_REFLECT);
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model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
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model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {1, 0, 1, 0});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({5, 4, 5, 6, 2, 1, 2, 3, 5, 4, 5, 6}));
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}
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TEST(MirrorPadTest, PadOneSide_right_Symmetric) {
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BaseMirrorPadOpModel<int> model(
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{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
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{TensorType_INT32, {}}, tflite::MirrorPadMode_SYMMETRIC);
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model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
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model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {0, 1, 0, 1});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({1, 2, 3, 3, 4, 5, 6, 6, 4, 5, 6, 6}));
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}
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TEST(MirrorPadTest, PadOneSide_left_Symmetric) {
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BaseMirrorPadOpModel<int> model(
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{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
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{TensorType_INT32, {}}, tflite::MirrorPadMode_SYMMETRIC);
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model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
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model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {1, 0, 1, 0});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({1, 1, 2, 3, 1, 1, 2, 3, 4, 4, 5, 6}));
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}
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TEST(MirrorPadTest, PadBothSides_Symmetric) {
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BaseMirrorPadOpModel<int> model(
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{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
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{TensorType_INT32, {}}, tflite::MirrorPadMode_SYMMETRIC);
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model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
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model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {1, 1, 1, 1});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({1, 1, 2, 3, 3, 1, 1, 2, 3, 3,
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4, 4, 5, 6, 6, 4, 4, 5, 6, 6}));
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}
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TEST(MirrorPadTest, PadBothSides_Reflect) {
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BaseMirrorPadOpModel<int> model(
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{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
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{TensorType_INT32, {}}, tflite::MirrorPadMode_REFLECT);
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model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
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model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {1, 1, 1, 1});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({5, 4, 5, 6, 5, 2, 1, 2, 3, 2,
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5, 4, 5, 6, 5, 2, 1, 2, 3, 2}));
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}
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TEST(MirrorPadTest, PadBothSides_Symmetric_Whole) {
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BaseMirrorPadOpModel<int> model(
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{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
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{TensorType_INT32, {}}, tflite::MirrorPadMode_SYMMETRIC);
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model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
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model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {2, 2, 3, 3});
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model.Invoke();
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({6, 5, 4, 4, 5, 6, 6, 5, 4, 3, 2, 1, 1, 2, 3, 3, 2, 1,
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3, 2, 1, 1, 2, 3, 3, 2, 1, 6, 5, 4, 4, 5, 6, 6, 5, 4,
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6, 5, 4, 4, 5, 6, 6, 5, 4, 3, 2, 1, 1, 2, 3, 3, 2, 1}));
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}
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TEST(MirrorPadTest, PadBothSides_Reflect_Whole) {
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BaseMirrorPadOpModel<int> model(
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{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
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{TensorType_INT32, {}}, tflite::MirrorPadMode_REFLECT);
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model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
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model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {1, 1, 2, 2});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({6, 5, 4, 5, 6, 5, 4, 3, 2, 1, 2, 3, 2, 1,
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6, 5, 4, 5, 6, 5, 4, 3, 2, 1, 2, 3, 2, 1}));
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}
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TEST(MirrorPadTest, Pad_Symmetric) {
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BaseMirrorPadOpModel<int> model(
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{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
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{TensorType_INT32, {}}, tflite::MirrorPadMode_SYMMETRIC);
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model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
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model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {1, 1, 2, 2});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({2, 1, 1, 2, 3, 3, 2, 2, 1, 1, 2, 3, 3, 2,
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5, 4, 4, 5, 6, 6, 5, 5, 4, 4, 5, 6, 6, 5}));
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}
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TEST(MirrorPadTest, Pad_1D_Reflect) {
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BaseMirrorPadOpModel<int> model(
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{TensorType_INT32, {3}}, {TensorType_INT32, {1, 2}},
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{TensorType_INT32, {}}, tflite::MirrorPadMode_REFLECT);
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model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3});
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model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {0, 2});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 2, 1}));
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}
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TEST(MirrorPadTest, Pad_1D_Symmetric) {
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BaseMirrorPadOpModel<int> model(
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{TensorType_INT32, {3}}, {TensorType_INT32, {1, 2}},
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{TensorType_INT32, {}}, tflite::MirrorPadMode_SYMMETRIC);
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model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3});
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model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {0, 2});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 3, 2}));
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}
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TEST(MirrorPadTest, Pad_1D_Symmetric_Multiple_Invoke) {
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BaseMirrorPadOpModel<int> model(
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{TensorType_INT32, {3}}, {TensorType_INT32, {1, 2}},
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{TensorType_INT32, {}}, tflite::MirrorPadMode_SYMMETRIC);
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model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3});
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model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {0, 2});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 3, 2}));
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model.PopulateTensor<int>(model.input_tensor_id(), {4, 5, 6});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({4, 5, 6, 6, 5}));
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}
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} // namespace
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} // namespace tflite
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