1 files changed, 127 insertions, 0 deletions
diff --git a/tensorflow/contrib/lite/kernels/mul_test.cc b/tensorflow/contrib/lite/kernels/mul_test.cc
new file mode 100644
index 0000000000..4b858e1f39
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/mul_test.cc
@@ -0,0 +1,127 @@
+/* Copyright 2017 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 <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;
+
+class BaseMulOpModel : public SingleOpModel {
+ public:
+  BaseMulOpModel(TensorData input, TensorData output,
+                 ActivationFunctionType activation_type) {
+    input1_ = AddInput(input);
+    input2_ = AddInput(input);
+    output_ = AddOutput(output);
+    SetBuiltinOp(BuiltinOperator_MUL, BuiltinOptions_MulOptions,
+                 CreateMulOptions(builder_, activation_type).Union());
+    BuildInterpreter({GetShape(input1_), GetShape(input2_)});
+  }
+
+  int input1() { return input1_; }
+  int input2() { return input2_; }
+
+ protected:
+  int input1_;
+  int input2_;
+  int output_;
+};
+
+class FloatMulOpModel : public BaseMulOpModel {
+ public:
+  using BaseMulOpModel::BaseMulOpModel;
+
+  std::vector<float> GetOutput() { return ExtractVector<float>(output_); }
+};
+
+// For quantized Mul, the error shouldn't exceed (2*step + step^2).
+// The param min=-1.0 & max=1.0 is used in the following tests.
+// The tolerance value is ~0.0157.
+const float kQuantizedStep = 2.0 / 255.0;
+const float kQuantizedTolerance =
+    2.0 * kQuantizedStep + kQuantizedStep * kQuantizedStep;
+
+class QuantizedMulOpModel : public BaseMulOpModel {
+ public:
+  using BaseMulOpModel::BaseMulOpModel;
+
+  std::vector<float> GetDequantizedOutput() {
+    return Dequantize<uint8_t>(ExtractVector<uint8_t>(output_),
+                               GetScale(output_), GetZeroPoint(output_));
+  }
+};
+
+TEST(FloatMulOpTest, NoActivation) {
+  FloatMulOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}},
+                    {TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
+  m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
+  m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5});
+  m.Invoke();
+  EXPECT_THAT(m.GetOutput(),
+              ElementsAreArray(ArrayFloatNear({-0.2, 0.04, 0.21, 0.4})));
+}
+
+TEST(FloatMulOpTest, ActivationRELU1) {
+  FloatMulOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}},
+                    {TensorType_FLOAT32, {}}, ActivationFunctionType_RELU1);
+  m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
+  m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 5});
+  m.Invoke();
+  EXPECT_THAT(m.GetOutput(),
+              ElementsAreArray(ArrayFloatNear({-0.2, 0.04, 0.21, 1.0})));
+}
+
+TEST(FloatMulOpTest, VariousInputShapes) {
+  std::vector<std::initializer_list<int>> test_shapes = {
+      {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
+  for (int i = 0; i < test_shapes.size(); ++i) {
+    FloatMulOpModel m({TensorType_FLOAT32, test_shapes[i]},
+                      {TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
+    m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0});
+    m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5, 1.1, 0.1});
+    m.Invoke();
+    EXPECT_THAT(
+        m.GetOutput(),
+        ElementsAreArray(ArrayFloatNear({-0.2, 0.04, 0.21, 0.4, 1.21, 0.2})))
+        << "With shape number " << i;
+  }
+}
+
+TEST(QuantizedMulOpTest, NoActivation) {
+  QuantizedMulOpModel m({TensorType_UINT8, {1, 2, 2, 1}, -1.0, 1.0},
+                        {TensorType_UINT8, {}, -1.0, 1.0},
+                        ActivationFunctionType_NONE);
+  m.QuantizeAndPopulate<uint8_t>(m.input1(), {-0.8, 0.2, 0.9, 0.7});
+  m.QuantizeAndPopulate<uint8_t>(m.input2(), {0.6, 0.4, 0.9, 0.8});
+  m.Invoke();
+  EXPECT_THAT(m.GetDequantizedOutput(),
+              ElementsAreArray(ArrayFloatNear({-0.48, 0.08, 0.81, 0.56},
+                                              kQuantizedTolerance)));
+}
+
+}  // namespace
+}  // namespace tflite
+
+int main(int argc, char** argv) {
+  // On Linux, add: tflite::LogToStderr();
+  tflite::LogToStderr();
+  ::testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}