wehub-resource-sync
440 lines
18 KiB
C++
440 lines
18 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 "tensorflow/lite/kernels/test_util.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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#include "tensorflow/lite/types/half.h"
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namespace tflite {
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namespace {
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using ::testing::ElementsAre;
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using ::testing::ElementsAreArray;
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template <typename T>
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class ReverseOpModel : public SingleOpModel {
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public:
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ReverseOpModel(const TensorData& input, const TensorData& axis) {
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input_ = AddInput(input);
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axis_ = AddInput(axis);
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output_ = AddOutput({input.type, {}});
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SetBuiltinOp(BuiltinOperator_REVERSE_V2, BuiltinOptions_ReverseV2Options,
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CreateReverseV2Options(builder_).Union());
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BuildInterpreter({GetShape(input_), GetShape(axis_)});
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}
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int input() { return input_; }
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int axis() { return axis_; }
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std::vector<T> GetOutput() { return ExtractVector<T>(output_); }
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std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
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private:
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int input_;
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int axis_;
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int output_;
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};
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// float32 tests.
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TEST(ReverseOpTest, FloatOneDimension) {
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ReverseOpModel<float> model({TensorType_FLOAT32, {4}},
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{TensorType_INT32, {1}});
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model.PopulateTensor<float>(model.input(), {1, 2, 3, 4});
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model.PopulateTensor<int32_t>(model.axis(), {0});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({4, 3, 2, 1}));
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}
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TEST(ReverseOpTest, FloatMultiDimensions) {
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ReverseOpModel<float> model({TensorType_FLOAT32, {4, 3, 2}},
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{TensorType_INT32, {1}});
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model.PopulateTensor<float>(model.input(),
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{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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model.PopulateTensor<int32_t>(model.axis(), {1});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4, 3, 2));
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({5, 6, 3, 4, 1, 2, 11, 12, 9, 10, 7, 8,
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17, 18, 15, 16, 13, 14, 23, 24, 21, 22, 19, 20}));
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}
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// int32 tests
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TEST(ReverseOpTest, Int32OneDimension) {
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ReverseOpModel<int32_t> model({TensorType_INT32, {4}},
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{TensorType_INT32, {1}});
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model.PopulateTensor<int32_t>(model.input(), {1, 2, 3, 4});
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model.PopulateTensor<int32_t>(model.axis(), {0});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({4, 3, 2, 1}));
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}
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TEST(ReverseOpTest, Int32MultiDimensions) {
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ReverseOpModel<int32_t> model({TensorType_INT32, {4, 3, 2}},
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{TensorType_INT32, {1}});
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model.PopulateTensor<int32_t>(
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model.input(), {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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model.PopulateTensor<int32_t>(model.axis(), {1});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4, 3, 2));
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({5, 6, 3, 4, 1, 2, 11, 12, 9, 10, 7, 8,
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17, 18, 15, 16, 13, 14, 23, 24, 21, 22, 19, 20}));
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}
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TEST(ReverseOpTest, Int32MultiDimensionsFirst) {
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ReverseOpModel<int32_t> model({TensorType_INT32, {3, 3, 3}},
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{TensorType_INT32, {1}});
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model.PopulateTensor<int32_t>(
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model.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
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15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27});
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model.PopulateTensor<int32_t>(model.axis(), {0});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 3, 3));
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({19, 20, 21, 22, 23, 24, 25, 26, 27, 10, 11, 12, 13, 14,
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15, 16, 17, 18, 1, 2, 3, 4, 5, 6, 7, 8, 9}));
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}
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TEST(ReverseOpTest, Int32MultiDimensionsSecond) {
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ReverseOpModel<int32_t> model({TensorType_INT32, {3, 3, 3}},
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{TensorType_INT32, {1}});
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model.PopulateTensor<int32_t>(
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model.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
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15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27});
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model.PopulateTensor<int32_t>(model.axis(), {1});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 3, 3));
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({7, 8, 9, 4, 5, 6, 1, 2, 3, 16, 17, 18, 13, 14,
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15, 10, 11, 12, 25, 26, 27, 22, 23, 24, 19, 20, 21}));
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}
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TEST(ReverseOpTest, Int32MultiDimensionsThird) {
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ReverseOpModel<int32_t> model({TensorType_INT32, {3, 3, 3}},
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{TensorType_INT32, {1}});
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model.PopulateTensor<int32_t>(
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model.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
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15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27});
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model.PopulateTensor<int32_t>(model.axis(), {2});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 3, 3));
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({3, 2, 1, 6, 5, 4, 9, 8, 7, 12, 11, 10, 15, 14,
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13, 18, 17, 16, 21, 20, 19, 24, 23, 22, 27, 26, 25}));
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}
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TEST(ReverseOpTest, Int32MultiDimensionsFirstSecond) {
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ReverseOpModel<int32_t> model({TensorType_INT32, {3, 3, 3}},
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{TensorType_INT32, {2}});
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model.PopulateTensor<int32_t>(
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model.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
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15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27});
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model.PopulateTensor<int32_t>(model.axis(), {0, 1});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 3, 3));
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({25, 26, 27, 22, 23, 24, 19, 20, 21, 16, 17, 18, 13, 14,
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15, 10, 11, 12, 7, 8, 9, 4, 5, 6, 1, 2, 3}));
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}
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TEST(ReverseOpTest, Int32MultiDimensionsSecondThird) {
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ReverseOpModel<int32_t> model({TensorType_INT32, {3, 3, 3}},
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{TensorType_INT32, {2}});
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model.PopulateTensor<int32_t>(
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model.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
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15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27});
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model.PopulateTensor<int32_t>(model.axis(), {1, 2});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 3, 3));
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({9, 8, 7, 6, 5, 4, 3, 2, 1, 18, 17, 16, 15, 14,
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13, 12, 11, 10, 27, 26, 25, 24, 23, 22, 21, 20, 19}));
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}
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TEST(ReverseOpTest, Int32MultiDimensionsSecondFirst) {
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ReverseOpModel<int32_t> model({TensorType_INT32, {3, 3, 3}},
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{TensorType_INT32, {2}});
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model.PopulateTensor<int32_t>(
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model.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
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15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27});
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model.PopulateTensor<int32_t>(model.axis(), {1, 0});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 3, 3));
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({25, 26, 27, 22, 23, 24, 19, 20, 21, 16, 17, 18, 13, 14,
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15, 10, 11, 12, 7, 8, 9, 4, 5, 6, 1, 2, 3}));
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}
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TEST(ReverseOpTest, Int32MultiDimensionsAll) {
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ReverseOpModel<int32_t> model({TensorType_INT32, {3, 3, 3}},
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{TensorType_INT32, {3}});
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model.PopulateTensor<int32_t>(
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model.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
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15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27});
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model.PopulateTensor<int32_t>(model.axis(), {0, 1, 2});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 3, 3));
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14,
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13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1}));
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}
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TEST(ReverseOpTest, Int32MultiDimensions8D) {
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ReverseOpModel<int32_t> model({TensorType_INT32, {1, 1, 1, 1, 1, 1, 1, 3}},
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{TensorType_INT32, {8}});
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model.PopulateTensor<int32_t>(model.input(), {1, 2, 3});
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model.PopulateTensor<int32_t>(model.axis(), {7, 6, 5, 4, 3, 2, 1, 0});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1, 1, 1, 1, 1, 1, 3));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({3, 2, 1}));
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}
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#if GTEST_HAS_DEATH_TEST
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TEST(ReverseOpTest, Int32MultiDimensions9D) {
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EXPECT_DEATH(
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ReverseOpModel<int32_t>({TensorType_INT32, {1, 1, 1, 1, 1, 1, 1, 1, 3}},
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{TensorType_INT32, {9}}),
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"Cannot allocate tensors");
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}
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#endif // GTEST_HAS_DEATH_TEST
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// int64 tests
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TEST(ReverseOpTest, Int64OneDimension) {
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ReverseOpModel<int64_t> model({TensorType_INT64, {4}},
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{TensorType_INT32, {1}});
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model.PopulateTensor<int64_t>(model.input(), {1, 2, 3, 4});
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model.PopulateTensor<int32_t>(model.axis(), {0});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({4, 3, 2, 1}));
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}
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TEST(ReverseOpTest, Int64MultiDimensions) {
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ReverseOpModel<int64_t> model({TensorType_INT64, {4, 3, 2}},
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{TensorType_INT32, {1}});
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model.PopulateTensor<int64_t>(
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model.input(), {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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model.PopulateTensor<int32_t>(model.axis(), {1});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4, 3, 2));
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({5, 6, 3, 4, 1, 2, 11, 12, 9, 10, 7, 8,
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17, 18, 15, 16, 13, 14, 23, 24, 21, 22, 19, 20}));
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}
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// uint8 tests
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TEST(ReverseOpTest, Uint8OneDimension) {
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ReverseOpModel<uint8_t> model({TensorType_UINT8, {4}},
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{TensorType_INT32, {1}});
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model.PopulateTensor<uint8_t>(model.input(), {1, 2, 3, 4});
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model.PopulateTensor<int32_t>(model.axis(), {0});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({4, 3, 2, 1}));
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}
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TEST(ReverseOpTest, Uint8MultiDimensions) {
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ReverseOpModel<uint8_t> model({TensorType_UINT8, {4, 3, 2}},
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{TensorType_INT32, {1}});
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model.PopulateTensor<uint8_t>(
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model.input(), {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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model.PopulateTensor<int32_t>(model.axis(), {1});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4, 3, 2));
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({5, 6, 3, 4, 1, 2, 11, 12, 9, 10, 7, 8,
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17, 18, 15, 16, 13, 14, 23, 24, 21, 22, 19, 20}));
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}
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// int8 tests
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TEST(ReverseOpTest, Int8OneDimension) {
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ReverseOpModel<int8_t> model({TensorType_INT8, {4}}, {TensorType_INT32, {1}});
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model.PopulateTensor<int8_t>(model.input(), {1, 2, -1, -2});
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model.PopulateTensor<int32_t>(model.axis(), {0});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({-2, -1, 2, 1}));
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}
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TEST(ReverseOpTest, Int8MultiDimensions) {
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ReverseOpModel<int8_t> model({TensorType_INT8, {4, 3, 2}},
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{TensorType_INT32, {1}});
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model.PopulateTensor<int8_t>(
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model.input(), {-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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model.PopulateTensor<int32_t>(model.axis(), {1});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4, 3, 2));
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({5, 6, -3, -4, -1, -2, 11, 12, 9, 10, 7, 8,
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17, 18, 15, 16, 13, 14, -23, -24, -21, -22, 19, 20}));
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}
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// int16 tests
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TEST(ReverseOpTest, Int16OneDimension) {
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ReverseOpModel<int16_t> model({TensorType_INT16, {4}},
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{TensorType_INT32, {1}});
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model.PopulateTensor<int16_t>(model.input(), {1, 2, 3, 4});
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model.PopulateTensor<int32_t>(model.axis(), {0});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({4, 3, 2, 1}));
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}
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TEST(ReverseOpTest, Int16MultiDimensions) {
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ReverseOpModel<int16_t> model({TensorType_INT16, {4, 3, 2}},
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{TensorType_INT32, {1}});
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model.PopulateTensor<int16_t>(
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model.input(), {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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model.PopulateTensor<int32_t>(model.axis(), {1});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4, 3, 2));
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({5, 6, 3, 4, 1, 2, 11, 12, 9, 10, 7, 8,
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17, 18, 15, 16, 13, 14, 23, 24, 21, 22, 19, 20}));
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}
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// float16 tests.
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TEST(ReverseOpTest, Float16OneDimension) {
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ReverseOpModel<half> model({TensorType_FLOAT16, {4}},
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{TensorType_INT32, {1}});
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model.PopulateTensor<half>(model.input(),
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{half(1), half(2), half(3), half(4)});
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model.PopulateTensor<int32_t>(model.axis(), {0});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4));
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({half(4), half(3), half(2), half(1)}));
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}
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TEST(ReverseOpTest, Float16MultiDimensions) {
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ReverseOpModel<half> model({TensorType_FLOAT16, {4, 3, 2}},
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{TensorType_INT32, {1}});
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model.PopulateTensor<half>(
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model.input(),
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{half(1), half(2), half(3), half(4), half(5), half(6),
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half(7), half(8), half(9), half(10), half(11), half(12),
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half(13), half(14), half(15), half(16), half(17), half(18),
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half(19), half(20), half(21), half(22), half(23), half(24)});
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model.PopulateTensor<int32_t>(model.axis(), {1});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4, 3, 2));
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({half(5), half(6), half(3), half(4), half(1),
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half(2), half(11), half(12), half(9), half(10),
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half(7), half(8), half(17), half(18), half(15),
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half(16), half(13), half(14), half(23), half(24),
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half(21), half(22), half(19), half(20)}));
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}
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// bfloat16 tests.
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TEST(ReverseOpTest, BFloat16OneDimension) {
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ReverseOpModel<Eigen::bfloat16> model({TensorType_BFLOAT16, {4}},
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{TensorType_INT32, {1}});
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model.PopulateTensor<Eigen::bfloat16>(
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model.input(), {Eigen::bfloat16(1), Eigen::bfloat16(2),
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Eigen::bfloat16(3), Eigen::bfloat16(4)});
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model.PopulateTensor<int32_t>(model.axis(), {0});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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|
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4));
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({Eigen::bfloat16(4), Eigen::bfloat16(3),
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|
Eigen::bfloat16(2), Eigen::bfloat16(1)}));
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|
}
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|
|
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TEST(ReverseOpTest, BFloat16MultiDimensions) {
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ReverseOpModel<Eigen::bfloat16> model({TensorType_BFLOAT16, {4, 3, 2}},
|
|
{TensorType_INT32, {1}});
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|
model.PopulateTensor<Eigen::bfloat16>(
|
|
model.input(),
|
|
{Eigen::bfloat16(1), Eigen::bfloat16(2), Eigen::bfloat16(3),
|
|
Eigen::bfloat16(4), Eigen::bfloat16(5), Eigen::bfloat16(6),
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|
Eigen::bfloat16(7), Eigen::bfloat16(8), Eigen::bfloat16(9),
|
|
Eigen::bfloat16(10), Eigen::bfloat16(11), Eigen::bfloat16(12),
|
|
Eigen::bfloat16(13), Eigen::bfloat16(14), Eigen::bfloat16(15),
|
|
Eigen::bfloat16(16), Eigen::bfloat16(17), Eigen::bfloat16(18),
|
|
Eigen::bfloat16(19), Eigen::bfloat16(20), Eigen::bfloat16(21),
|
|
Eigen::bfloat16(22), Eigen::bfloat16(23), Eigen::bfloat16(24)});
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|
model.PopulateTensor<int32_t>(model.axis(), {1});
|
|
ASSERT_EQ(model.Invoke(), kTfLiteOk);
|
|
|
|
EXPECT_THAT(model.GetOutputShape(), ElementsAre(4, 3, 2));
|
|
EXPECT_THAT(
|
|
model.GetOutput(),
|
|
ElementsAreArray(
|
|
{Eigen::bfloat16(5), Eigen::bfloat16(6), Eigen::bfloat16(3),
|
|
Eigen::bfloat16(4), Eigen::bfloat16(1), Eigen::bfloat16(2),
|
|
Eigen::bfloat16(11), Eigen::bfloat16(12), Eigen::bfloat16(9),
|
|
Eigen::bfloat16(10), Eigen::bfloat16(7), Eigen::bfloat16(8),
|
|
Eigen::bfloat16(17), Eigen::bfloat16(18), Eigen::bfloat16(15),
|
|
Eigen::bfloat16(16), Eigen::bfloat16(13), Eigen::bfloat16(14),
|
|
Eigen::bfloat16(23), Eigen::bfloat16(24), Eigen::bfloat16(21),
|
|
Eigen::bfloat16(22), Eigen::bfloat16(19), Eigen::bfloat16(20)}));
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|
}
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|
|
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} // namespace
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} // namespace tflite
|