tensorflow--tensorflow
wehub-resource-sync
299 行
11 KiB
C++
299 行
11 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 <initializer_list>
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#include <type_traits>
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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::ElementsAre;
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using ::testing::ElementsAreArray;
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template <typename T>
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class PackOpModel : public SingleOpModel {
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public:
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PackOpModel(const TensorData& input_template, int axis, int values_count) {
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std::vector<std::vector<int>> all_input_shapes;
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for (int i = 0; i < values_count; ++i) {
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all_input_shapes.push_back(input_template.shape);
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AddInput(input_template);
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}
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output_ = AddOutput({input_template.type, /*shape=*/{}, input_template.min,
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input_template.max});
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SetBuiltinOp(BuiltinOperator_PACK, BuiltinOptions_PackOptions,
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CreatePackOptions(builder_, values_count, axis).Union());
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BuildInterpreter(all_input_shapes);
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}
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void SetInput(int index, std::initializer_list<T> data) {
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PopulateTensor(index, data);
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}
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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 output_;
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};
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// float32 tests.
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TEST(PackOpTest, FloatThreeInputs) {
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PackOpModel<float> model({TensorType_FLOAT32, {2}}, 0, 3);
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model.SetInput(0, {1, 4});
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model.SetInput(1, {2, 5});
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model.SetInput(2, {3, 6});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 2));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 4, 2, 5, 3, 6}));
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}
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TEST(PackOpTest, FloatThreeInputsDifferentAxis) {
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PackOpModel<float> model({TensorType_FLOAT32, {2}}, 1, 3);
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model.SetInput(0, {1, 4});
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model.SetInput(1, {2, 5});
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model.SetInput(2, {3, 6});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 3));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6}));
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}
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TEST(PackOpTest, FloatThreeInputsNegativeAxis) {
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PackOpModel<float> model({TensorType_FLOAT32, {2}}, -1, 3);
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model.SetInput(0, {1, 4});
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model.SetInput(1, {2, 5});
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model.SetInput(2, {3, 6});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 3));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6}));
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}
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TEST(PackOpTest, FloatMultilDimensions) {
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PackOpModel<float> model({TensorType_FLOAT32, {2, 3}}, 1, 2);
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model.SetInput(0, {1, 2, 3, 4, 5, 6});
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model.SetInput(1, {7, 8, 9, 10, 11, 12});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 2, 3));
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({1, 2, 3, 7, 8, 9, 4, 5, 6, 10, 11, 12}));
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}
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TEST(PackOpTest, FloatFiveDimensions) {
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PackOpModel<float> model({TensorType_FLOAT32, {2, 2, 2, 2}}, 1, 2);
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model.SetInput(0, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16});
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model.SetInput(
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1, {17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 2, 2, 2, 2));
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({1, 2, 3, 4, 5, 6, 7, 8, 17, 18, 19,
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20, 21, 22, 23, 24, 9, 10, 11, 12, 13, 14,
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15, 16, 25, 26, 27, 28, 29, 30, 31, 32}));
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}
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// uint32 tests.
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TEST(PackOpTest, UInt32ThreeInputs) {
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PackOpModel<uint32_t> model({TensorType_UINT32, {2}}, 0, 3);
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model.SetInput(0, {1, 4});
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model.SetInput(1, {2, 5});
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model.SetInput(2, {3, 6});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 2));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 4, 2, 5, 3, 6}));
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}
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TEST(PackOpTest, UInt32ThreeInputsDifferentAxis) {
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PackOpModel<uint32_t> model({TensorType_UINT32, {2}}, 1, 3);
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model.SetInput(0, {1, 4});
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model.SetInput(1, {2, 5});
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model.SetInput(2, {3, 6});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 3));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6}));
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}
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TEST(PackOpTest, UInt32ThreeInputsNegativeAxis) {
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PackOpModel<uint32_t> model({TensorType_UINT32, {2}}, -1, 3);
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model.SetInput(0, {1, 4});
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model.SetInput(1, {2, 5});
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model.SetInput(2, {3, 6});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 3));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6}));
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}
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TEST(PackOpTest, UInt32MultilDimensions) {
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PackOpModel<uint32_t> model({TensorType_UINT32, {2, 3}}, 1, 2);
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model.SetInput(0, {1, 2, 3, 4, 5, 6});
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model.SetInput(1, {7, 8, 9, 10, 11, 12});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 2, 3));
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({1, 2, 3, 7, 8, 9, 4, 5, 6, 10, 11, 12}));
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}
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// int32 tests.
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TEST(PackOpTest, Int32ThreeInputs) {
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PackOpModel<int32_t> model({TensorType_INT32, {2}}, 0, 3);
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model.SetInput(0, {1, 4});
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model.SetInput(1, {2, 5});
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model.SetInput(2, {3, 6});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 2));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 4, 2, 5, 3, 6}));
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}
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TEST(PackOpTest, Int32ThreeInputsDifferentAxis) {
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PackOpModel<int32_t> model({TensorType_INT32, {2}}, 1, 3);
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model.SetInput(0, {1, 4});
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model.SetInput(1, {2, 5});
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model.SetInput(2, {3, 6});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 3));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6}));
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}
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TEST(PackOpTest, Int32ThreeInputsNegativeAxis) {
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PackOpModel<int32_t> model({TensorType_INT32, {2}}, -1, 3);
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model.SetInput(0, {1, 4});
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model.SetInput(1, {2, 5});
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model.SetInput(2, {3, 6});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 3));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6}));
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}
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TEST(PackOpTest, Int32MultilDimensions) {
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PackOpModel<int32_t> model({TensorType_INT32, {2, 3}}, 1, 2);
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model.SetInput(0, {1, 2, 3, 4, 5, 6});
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model.SetInput(1, {7, 8, 9, 10, 11, 12});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 2, 3));
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({1, 2, 3, 7, 8, 9, 4, 5, 6, 10, 11, 12}));
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}
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// int64 tests.
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TEST(PackOpTest, Int64ThreeInputs) {
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PackOpModel<int64_t> model({TensorType_INT64, {2}}, 0, 3);
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model.SetInput(0, {1LL << 33, 4});
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model.SetInput(1, {2, 5});
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model.SetInput(2, {3, -(1LL << 34)});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 2));
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({1LL << 33, 4LL, 2LL, 5LL, 3LL, -(1LL << 34)}));
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}
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TEST(PackOpTest, Int64ThreeInputsDifferentAxis) {
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PackOpModel<int64_t> model({TensorType_INT64, {2}}, 1, 3);
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model.SetInput(0, {1LL << 33, 4});
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model.SetInput(1, {2, 5});
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model.SetInput(2, {3, -(1LL << 34)});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 3));
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({1LL << 33, 2LL, 3LL, 4LL, 5LL, -(1LL << 34)}));
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}
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TEST(PackOpTest, Int64ThreeInputsNegativeAxis) {
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PackOpModel<int64_t> model({TensorType_INT64, {2}}, -1, 3);
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model.SetInput(0, {1LL << 33, 4});
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model.SetInput(1, {2, 5});
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model.SetInput(2, {3, -(1LL << 34)});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 3));
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({1LL << 33, 2LL, 3LL, 4LL, 5LL, -(1LL << 34)}));
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}
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TEST(PackOpTest, Int64MultilDimensions) {
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PackOpModel<int64_t> model({TensorType_INT64, {2, 3}}, 1, 2);
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model.SetInput(0, {1LL << 33, 2, 3, 4, 5, 6});
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model.SetInput(1, {7, 8, -(1LL << 34), 10, 11, 12});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 2, 3));
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({1LL << 33, 2LL, 3LL, 7LL, 8LL, -(1LL << 34),
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4LL, 5LL, 6LL, 10LL, 11LL, 12LL}));
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}
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template <typename InputType>
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struct PackOpTestInt : public ::testing::Test {
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using TypeToTest = InputType;
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TensorType TENSOR_TYPE =
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(std::is_same<InputType, int16_t>::value
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? TensorType_INT16
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: (std::is_same<InputType, uint8_t>::value ? TensorType_UINT8
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: TensorType_INT8));
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};
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using TestTypes = testing::Types<int8_t, uint8_t, int16_t>;
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TYPED_TEST_CASE(PackOpTestInt, TestTypes);
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TYPED_TEST(PackOpTestInt, ThreeInputs) {
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PackOpModel<typename TestFixture::TypeToTest> model(
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{TestFixture::TENSOR_TYPE, {2}}, 0, 3);
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model.SetInput(0, {1, 4});
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model.SetInput(1, {2, 5});
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model.SetInput(2, {3, 6});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 2));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 4, 2, 5, 3, 6}));
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}
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TYPED_TEST(PackOpTestInt, ThreeInputsDifferentAxis) {
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PackOpModel<typename TestFixture::TypeToTest> model(
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{TestFixture::TENSOR_TYPE, {2}}, 1, 3);
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model.SetInput(0, {1, 4});
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model.SetInput(1, {2, 5});
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model.SetInput(2, {3, 6});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 3));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6}));
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}
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TYPED_TEST(PackOpTestInt, ThreeInputsNegativeAxis) {
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PackOpModel<typename TestFixture::TypeToTest> model(
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{TestFixture::TENSOR_TYPE, {2}}, -1, 3);
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model.SetInput(0, {1, 4});
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model.SetInput(1, {2, 5});
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model.SetInput(2, {3, 6});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 3));
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6}));
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}
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TYPED_TEST(PackOpTestInt, MultilDimensions) {
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PackOpModel<typename TestFixture::TypeToTest> model(
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{TestFixture::TENSOR_TYPE, {2, 3}}, 1, 2);
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model.SetInput(0, {1, 2, 3, 4, 5, 6});
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model.SetInput(1, {7, 8, 9, 10, 11, 12});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 2, 3));
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({1, 2, 3, 7, 8, 9, 4, 5, 6, 10, 11, 12}));
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}
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} // namespace
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} // namespace tflite
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