9be3ad213b
* Added QI8 type support in tfl_ops.td * Added ReverseV2 op version 3 with int8 type support PiperOrigin-RevId: 348726608 Change-Id: I7003d7eff031e8ac12b55747fa5afaf9e3ab2a52
224 lines
8.0 KiB
C++
224 lines
8.0 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 <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::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_)});
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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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model.Invoke();
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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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model.Invoke();
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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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model.Invoke();
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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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model.Invoke();
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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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// 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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model.Invoke();
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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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model.Invoke();
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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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model.Invoke();
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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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model.Invoke();
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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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model.Invoke();
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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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model.Invoke();
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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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model.Invoke();
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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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model.Invoke();
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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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} // namespace
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} // namespace tflite
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