Automated g4 rollback of changelist 188525171

PiperOrigin-RevId: 189100846

1 files changed, 104 insertions, 0 deletions

diff --git a/tensorflow/contrib/lite/kernels/mfcc_test.cc b/tensorflow/contrib/lite/kernels/mfcc_test.cc
new file mode 100644
index 0000000000..0291ca8c1c
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/mfcc_test.cc
@@ -0,0 +1,104 @@
+/* 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 <functional>
+#include <memory>
+#include <vector>
+
+#include <gtest/gtest.h>
+#include "flatbuffers/flexbuffers.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 ops {
+namespace custom {
+
+TfLiteRegistration* Register_MFCC();
+
+namespace {
+
+using ::testing::ElementsAre;
+using ::testing::ElementsAreArray;
+
+class BaseMfccOpModel : public SingleOpModel {
+ public:
+  BaseMfccOpModel(const TensorData& input1, const TensorData& input2,
+                  const TensorData& output) {
+    input1_ = AddInput(input1);
+    input2_ = AddInput(input2);
+    output_ = AddOutput(output);
+
+    flexbuffers::Builder fbb;
+    fbb.Map([&]() {
+      fbb.Int("upper_frequency_limit", 4000);
+      fbb.Int("lower_frequency_limit", 20);
+      fbb.Int("filterbank_channel_count", 40);
+      fbb.Int("dct_coefficient_count", 13);
+    });
+    fbb.Finish();
+    SetCustomOp("Mfcc", fbb.GetBuffer(), Register_MFCC);
+
+    BuildInterpreter({GetShape(input1_), GetShape(input2_)});
+  }
+
+  int input1() { return input1_; }
+  int input2() { return input2_; }
+  std::vector<float> GetOutput() { return ExtractVector<float>(output_); }
+  std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
+
+ protected:
+  int input1_;
+  int input2_;
+  int output_;
+};
+
+TEST(MfccOpTest, SimpleTest) {
+  BaseMfccOpModel m({TensorType_FLOAT32, {1, 1, 513}}, {TensorType_INT32, {1}},
+                    {TensorType_FLOAT32, {}});
+
+  std::vector<float> data(513);
+  for (int i = 0; i < data.size(); ++i) {
+    data[i] = i + 1;
+  }
+  m.PopulateTensor<float>(m.input1(), 0, data.data(),
+                          data.data() + data.size());
+  m.PopulateTensor<int>(m.input2(), {22050});
+
+  m.Invoke();
+
+  std::vector<int> output_shape = m.GetOutputShape();
+  EXPECT_THAT(output_shape, ElementsAre(1, 1, 13));
+  EXPECT_THAT(
+      m.GetOutput(),
+      ElementsAreArray(ArrayFloatNear(
+          {29.13970072, -6.41568601, -0.61903012, -0.96778652, -0.26819878,
+           -0.40907028, -0.15614748, -0.23203119, -0.10481487, -0.1543029,
+           -0.0769791, -0.10806114, -0.06047613},
+          1e-3)));
+}
+
+}  // namespace
+}  // namespace custom
+}  // namespace ops
+}  // namespace tflite
+
+int main(int argc, char** argv) {
+  ::tflite::LogToStderr();
+  ::testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}