204945b19e
Segment identifiers in segment_sum should be in a 1-D tensor of same size as the first dimension of the input. The values of the tensor should be integers from {0, 1, 2, ... k-1}, where k is the first dimension of the input. The segment identifiers must not contain jumps and must be increasing.
See https://www.tensorflow.org/api_docs/python/tf/math#Segmentation as the source for these constraints.
PiperOrigin-RevId: 332510942
Change-Id: I898beaba00642c918bcd4b4d4ce893ebb190d869
147 lines
6.0 KiB
C++
147 lines
6.0 KiB
C++
/* Copyright 2020 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::ElementsAreArray;
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template <typename T>
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class SegmentSumOpModel : public SingleOpModel {
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public:
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SegmentSumOpModel(const TensorData& data, const TensorData& segment_ids) {
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data_id_ = AddInput(data);
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segment_ids_id_ = AddInput(segment_ids);
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output_id_ = AddOutput(data.type);
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SetBuiltinOp(BuiltinOperator_SEGMENT_SUM, BuiltinOptions_NONE, 0);
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BuildInterpreter({GetShape(data_id_), GetShape(segment_ids_id_)});
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}
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int data() const { return data_id_; }
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int segment_ids() const { return segment_ids_id_; }
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std::vector<T> GetOutput() { return ExtractVector<T>(output_id_); }
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std::vector<int32_t> GetOutputShape() { return GetTensorShape(output_id_); }
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protected:
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int data_id_;
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int segment_ids_id_;
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int output_id_;
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};
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TEST(SegmentSumOpModelTest, Int32Test_Simple) {
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SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 4}},
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{TensorType_INT32, {3}});
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model.PopulateTensor<int32_t>(model.data(),
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{1, 2, 3, 4, 4, 3, 2, 1, 5, 6, 7, 8});
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model.PopulateTensor<int32_t>(model.segment_ids(), {0, 0, 1});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({5, 5, 5, 5, 5, 6, 7, 8}));
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EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 4}));
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}
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TEST(SegmentSumOpModelTest, Int32Test_OneDimension) {
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SegmentSumOpModel<int32_t> model({TensorType_INT32, {3}},
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{TensorType_INT32, {3}});
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model.PopulateTensor<int32_t>(model.data(), {1, 2, 3});
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model.PopulateTensor<int32_t>(model.segment_ids(), {0, 0, 1});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({3, 3}));
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EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2}));
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}
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TEST(SegmentSumOpModelTest, Int32Test_ThreeDimensions) {
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SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2, 1}},
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{TensorType_INT32, {3}});
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model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
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model.PopulateTensor<int32_t>(model.segment_ids(), {0, 0, 1});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({4, 6, 5, 6}));
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EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 2, 1}));
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}
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TEST(SegmentSumOpModelTest, Float32Test_Simple) {
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SegmentSumOpModel<float> model({TensorType_FLOAT32, {3, 4}},
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{TensorType_INT32, {3}});
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model.PopulateTensor<float>(model.data(),
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{1, 2, 3, 4, 4, 3, 2, 1, 5, 6, 7, 8});
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model.PopulateTensor<int>(model.segment_ids(), {0, 0, 1});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({5.0f, 5.0f, 5.0f, 5.0f, 5.0f,
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6.0f, 7.0f, 8.0f}));
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EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 4}));
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}
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TEST(SegmentSumOpModelTest, Float32Test_OneDimension) {
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SegmentSumOpModel<float> model({TensorType_FLOAT32, {3}},
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{TensorType_INT32, {3}});
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model.PopulateTensor<float>(model.data(), {1, 2, 3});
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model.PopulateTensor<int32_t>(model.segment_ids(), {0, 0, 1});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({3.0f, 3.0f}));
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EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2}));
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}
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TEST(SegmentSumOpModelTest, Float32Test_ThreeDimensions) {
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SegmentSumOpModel<float> model({TensorType_FLOAT32, {3, 2, 1}},
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{TensorType_INT32, {3}});
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model.PopulateTensor<float>(model.data(), {1, 2, 3, 4, 5, 6});
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model.PopulateTensor<int32_t>(model.segment_ids(), {0, 0, 1});
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model.Invoke();
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EXPECT_THAT(model.GetOutput(), ElementsAreArray({4.0f, 6.0f, 5.0f, 6.0f}));
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EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 2, 1}));
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}
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TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNotSorted) {
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SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
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{TensorType_INT32, {3}});
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model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
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model.PopulateTensor<int32_t>(model.segment_ids(), {0, 3, 1});
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ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
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}
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TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNotConsecutive) {
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SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
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{TensorType_INT32, {3}});
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model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
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model.PopulateTensor<int32_t>(model.segment_ids(), {0, 3, 5});
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ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
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}
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TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNegative) {
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SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
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{TensorType_INT32, {3}});
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model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
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model.PopulateTensor<int32_t>(model.segment_ids(), {-1, 0, 1});
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ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
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}
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TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNotTheRightCardinality) {
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SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
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{TensorType_INT32, {2}});
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model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
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model.PopulateTensor<int32_t>(model.segment_ids(), {0, 1});
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ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
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}
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} // namespace
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} // namespace tflite
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