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+/*
+ * 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_UNPACK_H__
+#define __NNFW_CKER_UNPACK_H__
+
+#include "cker/Shape.h"
+#include "cker/Types.h"
+
+namespace nnfw
+{
+namespace cker
+{
+
+template <typename Scalar>
+void Unpack(const UnpackParams &params, const Shape &input_shape, const Scalar *input_data,
+ const Shape &output_shape, Scalar *const *output_datas)
+{
+ const int dimensions = input_shape.DimensionsCount();
+ const int outputs_count = params.num_split;
+
+ int outer_size = 1;
+ for (int i = 0; i < params.axis; i++)
+ {
+ outer_size *= input_shape.Dims(i);
+ }
+ int copy_size = 1;
+ for (int i = params.axis + 1; i < dimensions; i++)
+ {
+ copy_size *= input_shape.Dims(i);
+ }
+ assert(output_shape.FlatSize() == copy_size * outer_size);
+ UNUSED_RELEASE(output_shape);
+
+ for (int i = 0; i < outputs_count; ++i)
+ {
+ for (int k = 0; k < outer_size; k++)
+ {
+ Scalar *output_ptr = output_datas[i] + copy_size * k;
+ int loc = k * outputs_count * copy_size + i * copy_size;
+ memcpy(output_ptr, input_data + loc, copy_size * sizeof(Scalar));
+ }
+ }
+}
+
+} // namespace cker
+} // namespace nnfw
+
+#endif // __NNFW_CKER_UNPACK_H__