1 files changed, 204 insertions, 0 deletions
diff --git a/tensorflow/contrib/lite/kernels/arg_min_max.cc b/tensorflow/contrib/lite/kernels/arg_min_max.cc
new file mode 100644
index 0000000000..4f30d09030
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/arg_min_max.cc
@@ -0,0 +1,204 @@
+/* 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/quantization_util.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"
+
+namespace tflite {
+namespace ops {
+namespace builtin {
+namespace arg_min_max {
+
+constexpr int kInputTensor = 0;
+constexpr int kAxis = 1;
+constexpr int kOutputTensor = 0;
+
+TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+  TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
+  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
+
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* axis = GetInput(context, node, kAxis);
+  // Make sure the axis is only 1 dimension.
+  TF_LITE_ENSURE_EQ(context, NumElements(axis), 1);
+
+  // Make sure the axis is only either int32 or int64.
+  TF_LITE_ENSURE(context,
+                 axis->type == kTfLiteInt32 || axis->type == kTfLiteInt64);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+
+  auto* params = reinterpret_cast<TfLiteArgMaxParams*>(node->builtin_data);
+  switch (params->output_type) {
+    case kTfLiteInt32:
+      output->type = kTfLiteInt32;
+      break;
+    case kTfLiteInt64:
+      output->type = kTfLiteInt64;
+      break;
+    default:
+      context->ReportError(context, "Unknown index output data type: %d",
+                           params->output_type);
+      return kTfLiteError;
+  }
+
+  // Check conditions for different types.
+  switch (input->type) {
+    case kTfLiteFloat32:
+    case kTfLiteUInt8:
+    case kTfLiteInt32:
+      break;
+
+    default:
+      context->ReportError(
+          context,
+          "Unkonwn input type: %d, only float32 and int types are supported",
+          input->type);
+      return kTfLiteError;
+  }
+
+  // Copy the input dimensions to output except make the last dimension 1.
+  TF_LITE_ENSURE(context, NumDimensions(input) >= 1);
+  TfLiteIntArray* output_size = TfLiteIntArrayCopy(input->dims);
+  output_size->data[NumDimensions(input) - 1] = 1;
+
+  return context->ResizeTensor(context, output, output_size);
+}
+
+template <typename T>
+std::function<bool(T, T)> GetComparefunction(bool is_arg_max) {
+  if (is_arg_max) {
+    return std::greater<T>();
+  } else {
+    return std::less<T>();
+  }
+}
+
+// The current impl actually ignores the axis argument.
+// Only determine the index of the maximum value in the last dimension.
+TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node, bool is_arg_max) {
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const TfLiteTensor* axis = GetInput(context, node, kAxis);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+
+#define TF_LITE_ARG_MIN_MAX(data_type, axis_type, output_type)         \
+  optimized_ops::ArgMinMax(                                            \
+      GetTensorData<axis_type>(axis), GetTensorData<data_type>(input), \
+      GetTensorDims(input), GetTensorData<output_type>(output),        \
+      GetTensorDims(output), GetComparefunction<data_type>(is_arg_max))
+  if (axis->type == kTfLiteInt32) {
+    switch (output->type) {
+      case kTfLiteInt32: {
+        switch (input->type) {
+          case kTfLiteFloat32:
+            TF_LITE_ARG_MIN_MAX(float, int32_t, int32_t);
+            break;
+          case kTfLiteUInt8:
+            TF_LITE_ARG_MIN_MAX(uint8_t, int32_t, int32_t);
+            break;
+          case kTfLiteInt32:
+            TF_LITE_ARG_MIN_MAX(int32_t, int32_t, int32_t);
+            break;
+          default:
+            return kTfLiteError;
+        }
+      } break;
+      case kTfLiteInt64: {
+        switch (input->type) {
+          case kTfLiteFloat32:
+            TF_LITE_ARG_MIN_MAX(float, int32_t, int64_t);
+            break;
+          case kTfLiteUInt8:
+            TF_LITE_ARG_MIN_MAX(uint8_t, int32_t, int64_t);
+            break;
+          case kTfLiteInt32:
+            TF_LITE_ARG_MIN_MAX(int32_t, int32_t, int64_t);
+            break;
+          default:
+            return kTfLiteError;
+        }
+      } break;
+      default:
+        return kTfLiteError;
+    }
+  } else {
+    switch (output->type) {
+      case kTfLiteInt32: {
+        switch (input->type) {
+          case kTfLiteFloat32:
+            TF_LITE_ARG_MIN_MAX(float, int64_t, int32_t);
+            break;
+          case kTfLiteUInt8:
+            TF_LITE_ARG_MIN_MAX(uint8_t, int64_t, int32_t);
+            break;
+          case kTfLiteInt32:
+            TF_LITE_ARG_MIN_MAX(int32_t, int64_t, int32_t);
+            break;
+          default:
+            return kTfLiteError;
+        }
+      } break;
+      case kTfLiteInt64: {
+        switch (input->type) {
+          case kTfLiteFloat32:
+            TF_LITE_ARG_MIN_MAX(float, int64_t, int64_t);
+            break;
+          case kTfLiteUInt8:
+            TF_LITE_ARG_MIN_MAX(uint8_t, int64_t, int64_t);
+            break;
+          case kTfLiteInt32:
+            TF_LITE_ARG_MIN_MAX(int32_t, int64_t, int64_t);
+            break;
+          default:
+            return kTfLiteError;
+        }
+      } break;
+      default:
+        return kTfLiteError;
+    }
+  }
+#undef TF_LITE_ARG_MIN_MAX
+
+  return kTfLiteOk;
+}
+
+TfLiteStatus ArgMinEval(TfLiteContext* context, TfLiteNode* node) {
+  return Eval(context, node, false);
+}
+
+TfLiteStatus ArgMaxEval(TfLiteContext* context, TfLiteNode* node) {
+  return Eval(context, node, true);
+}
+
+}  // namespace arg_min_max
+
+TfLiteRegistration* Register_ARG_MAX() {
+  static TfLiteRegistration r = {nullptr, nullptr, arg_min_max::Prepare,
+                                 arg_min_max::ArgMaxEval};
+  return &r;
+}
+
+TfLiteRegistration* Register_ARG_MIN() {
+  static TfLiteRegistration r = {nullptr, nullptr, arg_min_max::Prepare,
+                                 arg_min_max::ArgMinEval};
+  return &r;
+}
+
+}  // namespace builtin
+}  // namespace ops
+}  // namespace tflite