1 files changed, 61 insertions, 0 deletions
diff --git a/compiler/nnc/backends/soft_backend/code_snippets/cpp_resize.def b/compiler/nnc/backends/soft_backend/code_snippets/cpp_resize.def
new file mode 100644
index 000000000..68dde5670
--- /dev/null
+++ b/compiler/nnc/backends/soft_backend/code_snippets/cpp_resize.def
@@ -0,0 +1,61 @@
+/* Copyright 2017 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.
+==============================================================================*/
+
+template <typename T>
+inline void ResizeNearestNeighbor(
+  const RuntimeShape& unextended_input_shape, const T* input_data,
+  const int32 output_height, const int32 output_width,
+  const RuntimeShape& unextended_output_shape, T* output_data) {
+  // Align corners = true is not supported.
+  TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4);
+  TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4);
+
+  const RuntimeShape input_shape =
+    RuntimeShape::ExtendedShape(4, unextended_input_shape);
+  const RuntimeShape output_shape =
+    RuntimeShape::ExtendedShape(4, unextended_output_shape);
+
+  int32 batches = MatchingDim(input_shape, 0, output_shape, 0);
+  int32 input_height = input_shape.Dims(1);
+  int32 input_width = input_shape.Dims(2);
+  int32 depth = MatchingDim(input_shape, 3, output_shape, 3);
+
+
+  // We use float to ensure agreement with the Tensorflow implementation.
+  const float height_scale = static_cast<float>(input_height) / output_height;
+  const float width_scale = static_cast<float>(input_width) / output_width;
+
+  const int col_offset = input_shape.Dims(3);
+  const int row_offset = input_shape.Dims(2) * col_offset;
+  const int batch_offset = input_shape.Dims(1) * row_offset;
+
+  const T* input_ptr = input_data;
+  T* output_ptr = output_data;
+  for (int b = 0; b < batches; ++b) {
+    for (int y = 0; y < output_height; ++y) {
+      int32 in_y = std::min(static_cast<int32>(std::floor(y * height_scale)),
+                            input_height - 1);
+      const T* y_input_ptr = input_ptr + in_y * row_offset;
+      for (int x = 0; x < output_width; ++x) {
+        int32 in_x = std::min(static_cast<int32>(std::floor(x * width_scale)),
+                              input_width - 1);
+        const T* x_input_ptr = y_input_ptr + in_x * col_offset;
+        memcpy(output_ptr, x_input_ptr, depth * sizeof(T));
+        output_ptr += depth;
+      }
+    }
+    input_ptr += batch_offset;
+  }
+}