STT-tensorflow/tensorflow/lite/kernels/mirror_pad_test.cc
Karim Nosir 143cad4d52 Optimize mirror_pad
PiperOrigin-RevId: 238510907
2019-03-14 14:11:52 -07:00

203 lines
8.3 KiB
C++

/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include <gtest/gtest.h>
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/kernels/register.h"
#include "tensorflow/lite/kernels/test_util.h"
#include "tensorflow/lite/model.h"
namespace tflite {
namespace {
using ::testing::ElementsAreArray;
template <typename T>
class BaseMirrorPadOpModel : public SingleOpModel {
public:
BaseMirrorPadOpModel(const TensorData& input,
const TensorData& padding_matrix,
const TensorData& output,
const tflite::MirrorPadMode mode) {
input_id_ = AddInput(input);
padding_matrix_id_ = AddInput(padding_matrix);
output_id_ = AddOutput(output);
SetBuiltinOp(BuiltinOperator_MIRROR_PAD, BuiltinOptions_MirrorPadOptions,
CreateMirrorPadOptions(builder_, mode).Union());
BuildInterpreter({GetShape(input_id_), GetShape(padding_matrix_id_)});
}
int input_tensor_id() { return input_id_; }
int padding_matrix_tensor_id() { return padding_matrix_id_; }
std::vector<T> GetOutput() { return ExtractVector<T>(output_id_); }
protected:
int input_id_;
int padding_matrix_id_;
int output_id_;
};
TEST(MirrorPadTest, EmptyPad) {
BaseMirrorPadOpModel<int> model(
{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
{TensorType_INT32, {}}, tflite::MirrorPadMode_REFLECT);
model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {0, 0, 0, 0});
model.Invoke();
EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6}));
}
TEST(MirrorPadTest, PadOneSide_right_Reflect) {
BaseMirrorPadOpModel<int> model(
{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
{TensorType_INT32, {}}, tflite::MirrorPadMode_REFLECT);
model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {0, 1, 0, 1});
model.Invoke();
EXPECT_THAT(model.GetOutput(),
ElementsAreArray({1, 2, 3, 2, 4, 5, 6, 5, 1, 2, 3, 2}));
}
TEST(MirrorPadTest, PadOneSide_left_Reflect) {
BaseMirrorPadOpModel<int> model(
{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
{TensorType_INT32, {}}, tflite::MirrorPadMode_REFLECT);
model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {1, 0, 1, 0});
model.Invoke();
EXPECT_THAT(model.GetOutput(),
ElementsAreArray({5, 4, 5, 6, 2, 1, 2, 3, 5, 4, 5, 6}));
}
TEST(MirrorPadTest, PadOneSide_right_Symmetric) {
BaseMirrorPadOpModel<int> model(
{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
{TensorType_INT32, {}}, tflite::MirrorPadMode_SYMMETRIC);
model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {0, 1, 0, 1});
model.Invoke();
EXPECT_THAT(model.GetOutput(),
ElementsAreArray({1, 2, 3, 3, 4, 5, 6, 6, 4, 5, 6, 6}));
}
TEST(MirrorPadTest, PadOneSide_left_Symmetric) {
BaseMirrorPadOpModel<int> model(
{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
{TensorType_INT32, {}}, tflite::MirrorPadMode_SYMMETRIC);
model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {1, 0, 1, 0});
model.Invoke();
EXPECT_THAT(model.GetOutput(),
ElementsAreArray({1, 1, 2, 3, 1, 1, 2, 3, 4, 4, 5, 6}));
}
TEST(MirrorPadTest, PadBothSides_Symmetric) {
BaseMirrorPadOpModel<int> model(
{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
{TensorType_INT32, {}}, tflite::MirrorPadMode_SYMMETRIC);
model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {1, 1, 1, 1});
model.Invoke();
EXPECT_THAT(model.GetOutput(),
ElementsAreArray({1, 1, 2, 3, 3, 1, 1, 2, 3, 3,
4, 4, 5, 6, 6, 4, 4, 5, 6, 6}));
}
TEST(MirrorPadTest, PadBothSides_Reflect) {
BaseMirrorPadOpModel<int> model(
{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
{TensorType_INT32, {}}, tflite::MirrorPadMode_REFLECT);
model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {1, 1, 1, 1});
model.Invoke();
EXPECT_THAT(model.GetOutput(),
ElementsAreArray({5, 4, 5, 6, 5, 2, 1, 2, 3, 2,
5, 4, 5, 6, 5, 2, 1, 2, 3, 2}));
}
TEST(MirrorPadTest, PadBothSides_Symmetric_Whole) {
BaseMirrorPadOpModel<int> model(
{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
{TensorType_INT32, {}}, tflite::MirrorPadMode_SYMMETRIC);
model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {2, 2, 3, 3});
model.Invoke();
EXPECT_THAT(
model.GetOutput(),
ElementsAreArray({6, 5, 4, 4, 5, 6, 6, 5, 4, 3, 2, 1, 1, 2, 3, 3, 2, 1,
3, 2, 1, 1, 2, 3, 3, 2, 1, 6, 5, 4, 4, 5, 6, 6, 5, 4,
6, 5, 4, 4, 5, 6, 6, 5, 4, 3, 2, 1, 1, 2, 3, 3, 2, 1}));
}
TEST(MirrorPadTest, PadBothSides_Reflect_Whole) {
BaseMirrorPadOpModel<int> model(
{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
{TensorType_INT32, {}}, tflite::MirrorPadMode_REFLECT);
model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {1, 1, 2, 2});
model.Invoke();
EXPECT_THAT(model.GetOutput(),
ElementsAreArray({6, 5, 4, 5, 6, 5, 4, 3, 2, 1, 2, 3, 2, 1,
6, 5, 4, 5, 6, 5, 4, 3, 2, 1, 2, 3, 2, 1}));
}
TEST(MirrorPadTest, Pad_Symmetric) {
BaseMirrorPadOpModel<int> model(
{TensorType_INT32, {2, 3}}, {TensorType_INT32, {2, 2}},
{TensorType_INT32, {}}, tflite::MirrorPadMode_SYMMETRIC);
model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3, 4, 5, 6});
model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {1, 1, 2, 2});
model.Invoke();
EXPECT_THAT(model.GetOutput(),
ElementsAreArray({2, 1, 1, 2, 3, 3, 2, 2, 1, 1, 2, 3, 3, 2,
5, 4, 4, 5, 6, 6, 5, 5, 4, 4, 5, 6, 6, 5}));
}
TEST(MirrorPadTest, Pad_1D_Reflect) {
BaseMirrorPadOpModel<int> model(
{TensorType_INT32, {3}}, {TensorType_INT32, {1, 2}},
{TensorType_INT32, {}}, tflite::MirrorPadMode_REFLECT);
model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3});
model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {0, 2});
model.Invoke();
EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 2, 1}));
}
TEST(MirrorPadTest, Pad_1D_Symmetric) {
BaseMirrorPadOpModel<int> model(
{TensorType_INT32, {3}}, {TensorType_INT32, {1, 2}},
{TensorType_INT32, {}}, tflite::MirrorPadMode_SYMMETRIC);
model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3});
model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {0, 2});
model.Invoke();
EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 3, 2}));
}
TEST(MirrorPadTest, Pad_1D_Symmetric_Multiple_Invoke) {
BaseMirrorPadOpModel<int> model(
{TensorType_INT32, {3}}, {TensorType_INT32, {1, 2}},
{TensorType_INT32, {}}, tflite::MirrorPadMode_SYMMETRIC);
model.PopulateTensor<int>(model.input_tensor_id(), {1, 2, 3});
model.PopulateTensor<int>(model.padding_matrix_tensor_id(), {0, 2});
model.Invoke();
EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 3, 2}));
model.PopulateTensor<int>(model.input_tensor_id(), {4, 5, 6});
model.Invoke();
EXPECT_THAT(model.GetOutput(), ElementsAreArray({4, 5, 6, 6, 5}));
}
} // namespace
} // namespace tflite