implementation of sparse_to_dense

PiperOrigin-RevId: 198710452

1 files changed, 155 insertions, 0 deletions

diff --git a/tensorflow/contrib/lite/kernels/sparse_to_dense_test.cc b/tensorflow/contrib/lite/kernels/sparse_to_dense_test.cc
new file mode 100644
index 0000000000..a51ec17afc
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/sparse_to_dense_test.cc
@@ -0,0 +1,155 @@
+
+/* Copyright 2018 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 <cstdarg>
+#include <gtest/gtest.h>
+#include "tensorflow/contrib/lite/interpreter.h"
+#include "tensorflow/contrib/lite/kernels/register.h"
+#include "tensorflow/contrib/lite/kernels/test_util.h"
+#include "tensorflow/contrib/lite/model.h"
+
+namespace tflite {
+namespace {
+
+using ::testing::ElementsAreArray;
+
+template <typename T>
+class SparseToDenseOpModel : public SingleOpModel {
+ public:
+  SparseToDenseOpModel(std::initializer_list<int> indices_shape,
+                       std::initializer_list<int> output_shape_shape,
+                       std::initializer_list<int> values_shape, T default_value,
+                       TensorType tensor_index_type,
+                       TensorType tensor_input_type) {
+    indices_ = AddInput(tensor_index_type);
+    output_shape_ = AddInput(TensorType_INT32);
+    values_ = AddInput(tensor_input_type);
+    default_value_ = AddInput(tensor_input_type);
+    output_ = AddOutput(tensor_input_type);
+
+    SetBuiltinOp(BuiltinOperator_SPARSE_TO_DENSE,
+                 BuiltinOptions_SparseToDenseOptions,
+                 CreateSparseToDenseOptions(builder_, false).Union());
+    BuildInterpreter({indices_shape, output_shape_shape, values_shape, {1}});
+
+    PopulateTensor<T>(default_value_, {default_value});
+  }
+
+  int indices() { return indices_; }
+  int output_shape() { return output_shape_; }
+  int values() { return values_; }
+
+  std::vector<T> GetOutput() { return ExtractVector<T>(output_); }
+  std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
+
+ private:
+  int indices_;
+  int output_shape_;
+  int values_;
+  int default_value_;
+  int output_;
+};
+
+TEST(SparseToDenseOpModelTest, ZeroDimensionTest) {
+  SparseToDenseOpModel<float> m({1}, {1}, {1}, 0, TensorType_INT32,
+                                TensorType_FLOAT32);
+  m.PopulateTensor<int32_t>(m.indices(), {3});
+  m.PopulateTensor<int32_t>(m.output_shape(), {5});
+  m.PopulateTensor<float>(m.values(), {7});
+  m.Invoke();
+
+  EXPECT_THAT(m.GetOutput(), ElementsAreArray({0, 0, 0, 7, 0}));
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({5}));
+}
+
+TEST(SparseToDenseOpModelTest, OneDimensionTest) {
+  SparseToDenseOpModel<float> m({3}, {1}, {3}, 0, TensorType_INT32,
+                                TensorType_FLOAT32);
+  m.PopulateTensor<int32_t>(m.indices(), {1, 3, 5});
+  m.PopulateTensor<int32_t>(m.output_shape(), {7});
+  m.PopulateTensor<float>(m.values(), {2, 4, 6});
+  m.Invoke();
+
+  EXPECT_THAT(m.GetOutput(), ElementsAreArray({0, 2, 0, 4, 0, 6, 0}));
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({7}));
+}
+
+TEST(SparseToDenseOpModelTest, TwoDimensionsTest) {
+  SparseToDenseOpModel<float> m({3, 3}, {3}, {3}, 0, TensorType_INT32,
+                                TensorType_FLOAT32);
+  m.PopulateTensor<int32_t>(m.indices(), {0, 0, 0, 1, 2, 1, 2, 0, 1});
+  m.PopulateTensor<int32_t>(m.output_shape(), {3, 3, 3});
+  m.PopulateTensor<float>(m.values(), {2, 4, 6});
+  m.Invoke();
+
+  EXPECT_THAT(m.GetOutput(),
+              ElementsAreArray({2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                                0, 0, 4, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0}));
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 3, 3}));
+}
+
+TEST(SparseToDenseOpModelTest, DefaultValueTest) {
+  SparseToDenseOpModel<float> m({3, 3}, {3}, {3}, -1, TensorType_INT32,
+                                TensorType_FLOAT32);
+  m.PopulateTensor<int32_t>(m.indices(), {0, 0, 0, 1, 2, 1, 2, 0, 1});
+  m.PopulateTensor<int32_t>(m.output_shape(), {3, 3, 3});
+  m.PopulateTensor<float>(m.values(), {2, 4, 6});
+  m.Invoke();
+
+  EXPECT_THAT(
+      m.GetOutput(),
+      ElementsAreArray({2,  -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
+                        -1, -1, 4,  -1, -1, 6,  -1, -1, -1, -1, -1, -1, -1}));
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 3, 3}));
+}
+
+TEST(SparseToDenseOpModelTest, IntegerValueTest) {
+  SparseToDenseOpModel<int32_t> m({3, 3}, {3}, {3}, -1, TensorType_INT32,
+                                  TensorType_INT32);
+  m.PopulateTensor<int32_t>(m.indices(), {0, 0, 0, 1, 2, 1, 2, 0, 1});
+  m.PopulateTensor<int32_t>(m.output_shape(), {3, 3, 3});
+  m.PopulateTensor<int32_t>(m.values(), {2, 4, 6});
+  m.Invoke();
+
+  EXPECT_THAT(
+      m.GetOutput(),
+      ElementsAreArray({2,  -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
+                        -1, -1, 4,  -1, -1, 6,  -1, -1, -1, -1, -1, -1, -1}));
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 3, 3}));
+}
+
+TEST(SparseToDenseOpModelTest, Int64IndexTest) {
+  SparseToDenseOpModel<float> m({3, 3}, {3}, {3}, -1, TensorType_INT64,
+                                TensorType_FLOAT32);
+  m.PopulateTensor<int64_t>(m.indices(), {0, 0, 0, 1, 2, 1, 2, 0, 1});
+  m.PopulateTensor<int32_t>(m.output_shape(), {3, 3, 3});
+  m.PopulateTensor<float>(m.values(), {2, 4, 6});
+  m.Invoke();
+
+  EXPECT_THAT(
+      m.GetOutput(),
+      ElementsAreArray({2,  -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
+                        -1, -1, 4,  -1, -1, 6,  -1, -1, -1, -1, -1, -1, -1}));
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3, 3, 3}));
+}
+
+}  // namespace
+}  // namespace tflite
+
+int main(int argc, char** argv) {
+  ::tflite::LogToStderr();
+  ::testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}