Kelvin resize nearest neighbors tfmicro

Change-Id: Ifd38bbb9a5ff66e89d70cc83c69ece71966378ed

tensorflow/lite/micro/kernels/kelvin/resize_nearest_neighbor.cc

diff --git a/tensorflow/lite/micro/kernels/kelvin/resize_nearest_neighbor.cc b/tensorflow/lite/micro/kernels/kelvin/resize_nearest_neighbor.cc
new file mode 100644
index 0000000..039bd0d
--- /dev/null
+++ b/tensorflow/lite/micro/kernels/kelvin/resize_nearest_neighbor.cc
@@ -0,0 +1,125 @@
+/* Copyright 2024 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/lite/kernels/internal/reference/resize_nearest_neighbor.h"
+
+#include "tensorflow/lite/c/builtin_op_data.h"
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
+#include "tensorflow/lite/kernels/kernel_util.h"
+#include "tensorflow/lite/kernels/op_macros.h"
+#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/micro_log.h"
+#include "tflm/opt/opt.h"
+
+namespace tflite {
+
+namespace {
+
+constexpr int kInputTensor = 0;
+constexpr int kSizeTensor = 1;
+constexpr int kOutputTensor = 0;
+
+TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+  MicroContext* micro_context = GetMicroContext(context);
+
+  TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
+  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
+
+  TfLiteTensor* input =
+      micro_context->AllocateTempInputTensor(node, kInputTensor);
+  TfLiteTensor* size =
+      micro_context->AllocateTempInputTensor(node, kSizeTensor);
+  TfLiteTensor* output =
+      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
+
+  // Our current implementations rely on the input being 4D,
+  // and the size being 1D tensor with exactly 2 elements.
+  TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
+  TF_LITE_ENSURE_EQ(context, NumDimensions(size), 1);
+  TF_LITE_ENSURE_EQ(context, size->type, kTfLiteInt32);
+  TF_LITE_ENSURE_EQ(context, size->dims->data[0], 2);
+
+  output->type = input->type;
+
+  if (!IsConstantTensor(size)) {
+    MicroPrintf("Dynamic tensors are unsupported in tfmicro.");
+    return kTfLiteError;
+  }
+
+  micro_context->DeallocateTempTfLiteTensor(input);
+  micro_context->DeallocateTempTfLiteTensor(size);
+  micro_context->DeallocateTempTfLiteTensor(output);
+
+  return kTfLiteOk;
+}
+
+TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+  auto* params =
+      reinterpret_cast<TfLiteResizeNearestNeighborParams*>(node->builtin_data);
+
+  const TfLiteEvalTensor* input =
+      tflite::micro::GetEvalInput(context, node, kInputTensor);
+  const TfLiteEvalTensor* size =
+      tflite::micro::GetEvalInput(context, node, kSizeTensor);
+  TfLiteEvalTensor* output =
+      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
+
+  tflite::ResizeNearestNeighborParams op_params;
+  op_params.align_corners = params->align_corners;
+  op_params.half_pixel_centers = false;
+
+  if (output->type == kTfLiteFloat32) {
+    reference_ops::ResizeNearestNeighbor(
+        op_params, tflite::micro::GetTensorShape(input),
+        tflite::micro::GetTensorData<int32_t>(input),
+        tflite::micro::GetTensorShape(size),
+        tflite::micro::GetTensorData<int32_t>(size),
+        tflite::micro::GetTensorShape(output),
+        tflite::micro::GetTensorData<int32_t>(output));
+  } else if (output->type == kTfLiteInt8) {
+
+    kelvin::opt::KelvinResizeNearestNeighbor(
+        op_params, tflite::micro::GetTensorShape(input),
+        tflite::micro::GetTensorData<int8_t>(input),
+        tflite::micro::GetTensorShape(size),
+        tflite::micro::GetTensorData<int32_t>(size),
+        tflite::micro::GetTensorShape(output),
+        tflite::micro::GetTensorData<int8_t>(output));
+  } else if (output->type == kTfLiteInt16) {
+    reference_ops::ResizeNearestNeighbor(
+        op_params, tflite::micro::GetTensorShape(input),
+        tflite::micro::GetTensorData<int16_t>(input),
+        tflite::micro::GetTensorShape(size),
+        tflite::micro::GetTensorData<int32_t>(size),
+        tflite::micro::GetTensorShape(output),
+        tflite::micro::GetTensorData<int16_t>(output));
+  } else {
+    MicroPrintf("Output tensor type %s (%d) not supported.",
+                TfLiteTypeGetName(output->type), output->type);
+
+    return kTfLiteError;
+  }
+
+  return kTfLiteOk;
+}
+
+}  // namespace
+
+TFLMRegistration Register_RESIZE_NEAREST_NEIGHBOR() {
+  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
+}
+
+}  // namespace tflite