Automated g4 rollback of changelist 188525171

PiperOrigin-RevId: 189100846

1 files changed, 165 insertions, 0 deletions

diff --git a/tensorflow/contrib/lite/kernels/audio_spectrogram.cc b/tensorflow/contrib/lite/kernels/audio_spectrogram.cc
new file mode 100644
index 0000000000..602f3888c1
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/audio_spectrogram.cc
@@ -0,0 +1,165 @@
+/* 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 "tensorflow/contrib/lite/builtin_op_data.h"
+#include "tensorflow/contrib/lite/context.h"
+#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h"
+#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h"
+#include "tensorflow/contrib/lite/kernels/internal/spectrogram.h"
+#include "tensorflow/contrib/lite/kernels/internal/tensor.h"
+#include "tensorflow/contrib/lite/kernels/kernel_util.h"
+#include "tensorflow/contrib/lite/kernels/op_macros.h"
+
+#include "flatbuffers/flexbuffers.h"
+
+namespace tflite {
+namespace ops {
+namespace custom {
+namespace audio_spectrogram {
+
+constexpr int kInputTensor = 0;
+constexpr int kOutputTensor = 0;
+
+enum KernelType {
+  kReference,
+};
+
+typedef struct {
+  int window_size;
+  int stride;
+  bool magnitude_squared;
+  int output_height;
+  internal::Spectrogram* spectrogram;
+} TfLiteAudioSpectrogramParams;
+
+void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+  auto* data = new TfLiteAudioSpectrogramParams;
+
+  const uint8_t* buffer_t = reinterpret_cast<const uint8_t*>(buffer);
+
+  const flexbuffers::Map& m = flexbuffers::GetRoot(buffer_t, length).AsMap();
+  data->window_size = m["window_size"].AsInt64();
+  data->stride = m["stride"].AsInt64();
+  data->magnitude_squared = m["magnitude_squared"].AsBool();
+
+  data->spectrogram = new internal::Spectrogram;
+
+  return data;
+}
+
+void Free(TfLiteContext* context, void* buffer) {
+  auto* params = reinterpret_cast<TfLiteAudioSpectrogramParams*>(buffer);
+  delete params->spectrogram;
+  delete params;
+}
+
+TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+  auto* params =
+      reinterpret_cast<TfLiteAudioSpectrogramParams*>(node->user_data);
+
+  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
+  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
+
+  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+
+  TF_LITE_ENSURE_EQ(context, NumDimensions(input), 2);
+
+  TF_LITE_ENSURE_EQ(context, output->type, kTfLiteFloat32);
+  TF_LITE_ENSURE_EQ(context, input->type, output->type);
+
+  TF_LITE_ENSURE(context, params->spectrogram->Initialize(params->window_size,
+                                                          params->stride));
+  const int64_t sample_count = input->dims->data[0];
+  const int64_t length_minus_window = (sample_count - params->window_size);
+  if (length_minus_window < 0) {
+    params->output_height = 0;
+  } else {
+    params->output_height = 1 + (length_minus_window / params->stride);
+  }
+  TfLiteIntArray* output_size = TfLiteIntArrayCreate(3);
+  output_size->data[0] = input->dims->data[1];
+  output_size->data[1] = params->output_height;
+  output_size->data[2] = params->spectrogram->output_frequency_channels();
+
+  return context->ResizeTensor(context, output, output_size);
+}
+
+template <KernelType kernel_type>
+TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+  auto* params =
+      reinterpret_cast<TfLiteAudioSpectrogramParams*>(node->user_data);
+
+  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+
+  TF_LITE_ENSURE(context, params->spectrogram->Initialize(params->window_size,
+                                                          params->stride));
+
+  const float* input_data = GetTensorData<float>(input);
+
+  const int64_t sample_count = input->dims->data[0];
+  const int64_t channel_count = input->dims->data[1];
+
+  const int64_t output_width = params->spectrogram->output_frequency_channels();
+
+  float* output_flat = GetTensorData<float>(output);
+
+  std::vector<float> input_for_channel(sample_count);
+  for (int64_t channel = 0; channel < channel_count; ++channel) {
+    float* output_slice =
+        output_flat + (channel * params->output_height * output_width);
+    for (int i = 0; i < sample_count; ++i) {
+      input_for_channel[i] = input_data[i * channel_count + channel];
+    }
+    std::vector<std::vector<float>> spectrogram_output;
+    TF_LITE_ENSURE(context,
+                   params->spectrogram->ComputeSquaredMagnitudeSpectrogram(
+                       input_for_channel, &spectrogram_output));
+    TF_LITE_ENSURE_EQ(context, spectrogram_output.size(),
+                      params->output_height);
+    TF_LITE_ENSURE(context, spectrogram_output.empty() ||
+                                (spectrogram_output[0].size() == output_width));
+    for (int row_index = 0; row_index < params->output_height; ++row_index) {
+      const std::vector<float>& spectrogram_row = spectrogram_output[row_index];
+      TF_LITE_ENSURE_EQ(context, spectrogram_row.size(), output_width);
+      float* output_row = output_slice + (row_index * output_width);
+      if (params->magnitude_squared) {
+        for (int i = 0; i < output_width; ++i) {
+          output_row[i] = spectrogram_row[i];
+        }
+      } else {
+        for (int i = 0; i < output_width; ++i) {
+          output_row[i] = sqrtf(spectrogram_row[i]);
+        }
+      }
+    }
+  }
+  return kTfLiteOk;
+}
+
+}  // namespace audio_spectrogram
+
+TfLiteRegistration* Register_AUDIO_SPECTROGRAM() {
+  static TfLiteRegistration r = {
+      audio_spectrogram::Init, audio_spectrogram::Free,
+      audio_spectrogram::Prepare,
+      audio_spectrogram::Eval<audio_spectrogram::kReference>};
+  return &r;
+}
+
+}  // namespace custom
+}  // namespace ops
+}  // namespace tflite