1 files changed, 104 insertions, 0 deletions
diff --git a/compute/cker/include/cker/operation/MaxMin.h b/compute/cker/include/cker/operation/MaxMin.h
new file mode 100644
index 000000000..691b3b0b3
--- /dev/null
+++ b/compute/cker/include/cker/operation/MaxMin.h
@@ -0,0 +1,104 @@
+/*
+ * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
+ * 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.
+ */
+
+#ifndef __NNFW_CKER_MAXMIN_H__
+#define __NNFW_CKER_MAXMIN_H__
+
+#include "cker/Shape.h"
+#include "cker/Utils.h"
+
+namespace nnfw
+{
+namespace cker
+{
+
+struct MaximumOp
+{
+  template <typename data_type> static data_type op(data_type el1, data_type el2)
+  {
+    return el1 > el2 ? el1 : el2;
+  }
+};
+
+struct MinimumOp
+{
+  template <typename data_type> static data_type op(data_type el1, data_type el2)
+  {
+    return el1 < el2 ? el1 : el2;
+  }
+};
+
+template <typename T, typename Op>
+inline void
+MaximumMinimumBroadcast4DSlow(const Shape &unextended_input1_shape, const T *input1_data,
+                              const Shape &unextended_input2_shape, const T *input2_data,
+                              const Shape &unextended_output_shape, T *output_data, Op op)
+{
+  assert(unextended_input1_shape.DimensionsCount() <= 4);
+  assert(unextended_input2_shape.DimensionsCount() <= 4);
+  assert(unextended_output_shape.DimensionsCount() <= 4);
+  const Shape output_shape = Shape::ExtendedShape(4, unextended_output_shape);
+
+  NdArrayDesc<4> desc1;
+  NdArrayDesc<4> desc2;
+  NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, unextended_input2_shape, &desc1,
+                                      &desc2);
+
+  for (int b = 0; b < output_shape.Dims(0); ++b)
+  {
+    for (int y = 0; y < output_shape.Dims(1); ++y)
+    {
+      for (int x = 0; x < output_shape.Dims(2); ++x)
+      {
+        for (int c = 0; c < output_shape.Dims(3); ++c)
+        {
+          auto out_idx = Offset(output_shape, b, y, x, c);
+          auto in1_idx = SubscriptToIndex(desc1, b, y, x, c);
+          auto in2_idx = SubscriptToIndex(desc2, b, y, x, c);
+          auto in1_val = input1_data[in1_idx];
+          auto in2_val = input2_data[in2_idx];
+          output_data[out_idx] = op(in1_val, in2_val);
+        }
+      }
+    }
+  }
+}
+
+template <typename T>
+inline void Max(const Shape &unextended_input1_shape, const T *input1_data,
+                const Shape &unextended_input2_shape, const T *input2_data,
+                const Shape &unextended_output_shape, T *output_data)
+{
+  MaximumMinimumBroadcast4DSlow<T>(unextended_input1_shape, input1_data, unextended_input2_shape,
+                                   input2_data, unextended_output_shape, output_data,
+                                   MaximumOp::template op<T>);
+}
+
+template <typename T>
+inline void Min(const Shape &unextended_input1_shape, const T *input1_data,
+                const Shape &unextended_input2_shape, const T *input2_data,
+                const Shape &unextended_output_shape, T *output_data)
+{
+  MaximumMinimumBroadcast4DSlow<T>(unextended_input1_shape, input1_data, unextended_input2_shape,
+                                   input2_data, unextended_output_shape, output_data,
+                                   MinimumOp::template op<T>);
+}
+
+} // namespace cker
+} // namespace nnfw
+
+#endif // __NNFW_CKER_MAXMIN_H__