diff options
Diffstat (limited to 'tensorflow_lite_support/custom_ops/kernel/unsorted_segment_min_test.cc')
| -rw-r--r-- | tensorflow_lite_support/custom_ops/kernel/unsorted_segment_min_test.cc | 137 |
1 files changed, 137 insertions, 0 deletions
diff --git a/tensorflow_lite_support/custom_ops/kernel/unsorted_segment_min_test.cc b/tensorflow_lite_support/custom_ops/kernel/unsorted_segment_min_test.cc new file mode 100644 index 00000000..bb4d4141 --- /dev/null +++ b/tensorflow_lite_support/custom_ops/kernel/unsorted_segment_min_test.cc @@ -0,0 +1,137 @@ +/* Copyright 2022 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 <limits.h> +#include <stdint.h> + +#include <initializer_list> +#include <vector> + +#include "testing/base/public/gunit.h" +#include "tensorflow/lite/kernels/test_util.h" +#include "tensorflow/lite/kernels/unsorted_segment_test.h" +#include "tensorflow/lite/schema/schema_generated.h" + +namespace tflite { +namespace { + +using ::testing::ElementsAreArray; + +template <typename T> +class UnsortedSegmentMinModel : public UnsortedSegmentModel<T> { + public: + UnsortedSegmentMinModel(const TensorData& data, const TensorData& segment_ids, + const TensorData& num_segments) + : UnsortedSegmentModel<T>(data, segment_ids, num_segments, + BuiltinOperator_UNSORTED_SEGMENT_MIN, + BuiltinOptions_NONE) {} + + explicit UnsortedSegmentMinModel( + const TensorData& data, const std::initializer_list<int>& segment_id_data, + const std::initializer_list<int>& segment_id_shape, + const std::initializer_list<int>& num_segments_data, + const std::initializer_list<int>& num_segments_shape) + : UnsortedSegmentModel<T>(data, segment_id_data, segment_id_shape, + num_segments_data, num_segments_shape, + BuiltinOperator_UNSORTED_SEGMENT_MIN, + BuiltinOptions_NONE) {} +}; + +INSTANTIATE_TEST_SUITE_P(UnsortedSegmentMinTestP, UnsortedSegmentTest, + testing::Values(BuiltinOperator_UNSORTED_SEGMENT_MIN)); + +TEST(UnsortedSegmentMinModelTest, Int32Test_Simple) { + UnsortedSegmentMinModel<int32_t> model({TensorType_INT32, {6}}, + {TensorType_INT32, {6}}, + {TensorType_INT32, {1}}); + model.PopulateTensor<int32_t>(model.data(), {5, 3, 7, 8, 6, 4}); + model.PopulateTensor<int32_t>(model.segment_ids(), {0, 0, 1, 1, 0, 1}); + model.PopulateTensor<int32_t>(model.num_segments(), {2}); + ASSERT_EQ(model.Invoke(), kTfLiteOk); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({3, 4})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2})); +} + +TEST(UnsortedSegmentMinModelTest, Int32Test_Simple2D) { + UnsortedSegmentMinModel<int32_t> model({TensorType_INT32, {3, 4}}, + {TensorType_INT32, {3}}, + {TensorType_INT32, {1}}); + model.PopulateTensor<int32_t>(model.data(), + {1, 2, 3, 4, 5, 6, 7, 8, 4, 3, 2, 1}); + model.PopulateTensor<int32_t>(model.segment_ids(), {0, 1, 0}); + model.PopulateTensor<int32_t>(model.num_segments(), {2}); + ASSERT_EQ(model.Invoke(), kTfLiteOk); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 2, 1, 5, 6, 7, 8})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 4})); +} + +TEST(UnsortedSegmentMinModelTest, FloatTest_Simple) { + UnsortedSegmentMinModel<float> model({TensorType_FLOAT32, {8}}, + {TensorType_INT32, {8}}, + {TensorType_INT32, {1}}); + model.PopulateTensor<float>(model.data(), + {1.0, 2.0, 3.0, 4.0, 4.0, 3.0, 2.0, 1.0}); + model.PopulateTensor<int32_t>(model.segment_ids(), {1, 0, 1, 7, 7, 7, 7, 7}); + model.PopulateTensor<int32_t>(model.num_segments(), {8}); + ASSERT_EQ(model.Invoke(), kTfLiteOk); + EXPECT_THAT( + model.GetOutput(), + ElementsAreArray(ArrayFloatNear( + {2.0, 1.0, std::numeric_limits<float>::max(), + std::numeric_limits<float>::max(), std::numeric_limits<float>::max(), + std::numeric_limits<float>::max(), std::numeric_limits<float>::max(), + 1.0}))); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({8})); +} + +TEST(UnsortedSegmentMinModelTest, FloatTest_Simple2D) { + UnsortedSegmentMinModel<float> model({TensorType_FLOAT32, {3, 4}}, + {TensorType_INT32, {3}}, + {TensorType_INT32, {1}}); + model.PopulateTensor<float>(model.data(), {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, + 8.0, 4.0, 3.0, 2.0, 1.0}); + model.PopulateTensor<int32_t>(model.segment_ids(), {0, 1, 0}); + model.PopulateTensor<int32_t>(model.num_segments(), {2}); + ASSERT_EQ(model.Invoke(), kTfLiteOk); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray( + ArrayFloatNear({1.0, 2.0, 2.0, 1.0, 5.0, 6.0, 7.0, 8.0}))); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 4})); +} + +TEST(UnsortedSegmentMinModelTest, SegmentsAreNegative) { + UnsortedSegmentMinModel<int32_t> model({TensorType_INT32, {2, 2}}, + {TensorType_INT32, {2}}, + {TensorType_INT32, {1}}); + model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4}); + model.PopulateTensor<int32_t>(model.segment_ids(), {-1, -1}); + model.PopulateTensor<int32_t>(model.num_segments(), {1}); + ASSERT_EQ(model.Invoke(), kTfLiteOk); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray({std::numeric_limits<int32_t>::max(), + std::numeric_limits<int32_t>::max()})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 2})); +} + +TEST(UnsortedSegmentMinModelTest, ConstParamenterTest) { + UnsortedSegmentMinModel<int32_t> model({TensorType_INT32, {3, 2}}, {0, 1, 0}, + {3}, {2}, {1}); + model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6}); + ASSERT_EQ(model.Invoke(), kTfLiteOk); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 4})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 2})); +} + +} // namespace +} // namespace tflite
\ No newline at end of file |