1 files changed, 130 insertions, 0 deletions
diff --git a/tensorflow/contrib/lite/kernels/unpack.cc b/tensorflow/contrib/lite/kernels/unpack.cc
new file mode 100644
index 0000000000..9ff06f8331
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+++ b/tensorflow/contrib/lite/kernels/unpack.cc
@@ -0,0 +1,130 @@
+/* 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/c/builtin_op_data.h"
+#include "tensorflow/contrib/lite/c/c_api_internal.h"
+#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h"
+#include "tensorflow/contrib/lite/kernels/internal/tensor.h"
+#include "tensorflow/contrib/lite/kernels/kernel_util.h"
+
+namespace tflite {
+namespace ops {
+namespace builtin {
+namespace unpack {
+namespace {
+
+constexpr int kInputTensor = 0;
+
+// Op data for unpack op.
+struct OpData {
+  int num;
+  int axis;
+};
+
+void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+  auto* data = new OpData;
+  data->axis = 0;
+  return data;
+}
+
+void Free(TfLiteContext* context, void* buffer) {
+  delete reinterpret_cast<OpData*>(buffer);
+}
+
+TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+  const OpData* data = reinterpret_cast<OpData*>(node->builtin_data);
+
+  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
+  TF_LITE_ENSURE_EQ(context, NumOutputs(node), data->num);
+
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  TF_LITE_ENSURE(context, NumDimensions(input) <= 4);
+  TF_LITE_ENSURE(context, NumDimensions(input) > 1);
+  TF_LITE_ENSURE(context, NumDimensions(input) > data->axis);
+  // TODO(renjieliu): Support negative axis.
+  TF_LITE_ENSURE(context, data->axis >= 0);
+  if (input->type != kTfLiteInt32 && input->type != kTfLiteFloat32) {
+    context->ReportError(context,
+                         "Currently pack only supports int32 and float32.");
+    return kTfLiteError;
+  }
+
+  const TfLiteIntArray* input_shape = input->dims;
+  // Num should be equal to the shape[axis].
+  // Resize outputs. rank will be R - 1.
+  TfLiteIntArray* output_shape = TfLiteIntArrayCreate(NumDimensions(input) - 1);
+  int o = 0;
+  for (int index = 0; index < NumDimensions(input); ++index) {
+    if (index != data->axis) {
+      output_shape->data[o++] = input_shape->data[index];
+    }
+  }
+
+  TF_LITE_ENSURE_EQ(context, data->num, input_shape->data[data->axis]);
+  for (int i = 0; i < data->num; ++i) {
+    TfLiteIntArray* copied_output_shape = TfLiteIntArrayCopy(output_shape);
+    TfLiteTensor* output = GetOutput(context, node, i);
+    TF_LITE_ENSURE_EQ(context, output->type, input->type);
+    TF_LITE_ENSURE_OK(
+        context, context->ResizeTensor(context, output, copied_output_shape));
+  }
+
+  TfLiteIntArrayFree(output_shape);
+  return kTfLiteOk;
+}
+
+template <typename T>
+void UnpackImpl(TfLiteContext* context, TfLiteNode* node,
+                const TfLiteTensor* input, int output_count, int axis) {
+  VectorOfTensors<T> all_outputs(*context, *node->outputs);
+  reference_ops::Unpack<T>(axis, GetTensorData<T>(input), GetTensorDims(input),
+                           NumDimensions(input), output_count,
+                           all_outputs.data(), **all_outputs.dims());
+}
+
+TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+  const OpData* data = reinterpret_cast<OpData*>(node->builtin_data);
+
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  switch (input->type) {
+    case kTfLiteFloat32: {
+      UnpackImpl<float>(context, node, input, data->num, data->axis);
+      break;
+    }
+    case kTfLiteInt32: {
+      UnpackImpl<int32_t>(context, node, input, data->num, data->axis);
+      break;
+    }
+    default: {
+      context->ReportError(context,
+                           "Currently pack only supports int32 and float32.");
+      return kTfLiteError;
+    }
+  }
+
+  return kTfLiteOk;
+}
+}  // namespace
+}  // namespace unpack
+
+TfLiteRegistration* Register_UNPACK() {
+  static TfLiteRegistration r = {unpack::Init, unpack::Free, unpack::Prepare,
+                                 unpack::Eval};
+  return &r;
+}
+
+}  // namespace builtin
+}  // namespace ops
+}  // namespace tflite