1 files changed, 0 insertions, 150 deletions
diff --git a/runtime/neurun/core/src/exec/interp/operations/Concat.cc b/runtime/neurun/core/src/exec/interp/operations/Concat.cc
deleted file mode 100644
index a127e5f30..000000000
--- a/runtime/neurun/core/src/exec/interp/operations/Concat.cc
+++ /dev/null
@@ -1,150 +0,0 @@
-/*
- * Copyright (c) 2019 Samsung Electronics Co., Ltd. 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 <cker/operation/Concatenation.h>
-
-#include "OperationUtil.h"
-
-#include "exec/interp/Registration.h"
-#include "ir/operation/Concat.h"
-#include "misc/polymorphic_downcast.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-namespace concat
-{
-
-void prepareConcat(ExecEnv *env, const ir::Operation &node)
-{
-  const auto &concat_node = nnfw::misc::polymorphic_downcast<const ir::operation::Concat &>(node);
-
-  const auto first_index = node.getInputs().at(0);
-  const auto out_index = node.getOutputs().at(0);
-
-  const auto first_tensor = env->tensorAt(first_index);
-  uint32_t out_axis_dimension = 0;
-  const int32_t axis_raw = concat_node.param().axis;
-  const uint32_t axis = (axis_raw < 0) ? (axis_raw + first_tensor->num_dimensions()) : axis_raw;
-
-  // All inputs shape should be same except axis dimension
-  // All inputs type should be same
-  for (auto input : node.getInputs())
-  {
-    assert(first_tensor->num_dimensions() == env->tensorAt(input)->num_dimensions());
-    assert(first_tensor->data_type() == env->tensorAt(input)->data_type());
-    for (uint32_t i = 0; i < first_tensor->num_dimensions(); i++)
-    {
-      if (i == axis)
-      {
-        out_axis_dimension += env->tensorAt(input)->dimension(i);
-        continue;
-      }
-      assert(first_tensor->dimension(i) == env->tensorAt(input)->dimension(i));
-    }
-  }
-
-  // Make output tensor info using first input tensor info, and accumulated axis dimension value
-  auto out_shape = first_tensor->tensorInfo().shape();
-  out_shape.dim(axis) = out_axis_dimension;
-  env->allocateIfNeeded(out_index,
-                        ir::OperandInfo{out_shape, first_tensor->tensorInfo().typeInfo()});
-
-  auto out_tensor = env->tensorAt(out_index);
-  UNUSED_RELEASE(out_tensor);
-
-  // Output shape should be same with input except axis dimension
-  // Output type should be same with input
-  assert(first_tensor->data_type() == out_tensor->data_type());
-  for (uint32_t i = 0; i < first_tensor->num_dimensions(); i++)
-  {
-    if (i == axis)
-    {
-      continue;
-    }
-    assert(first_tensor->dimension(i) == out_tensor->dimension(i));
-  }
-}
-
-void invoke(const std::vector<const ITensor *> in_tensors, const ITensor *out_tensor, uint32_t axis)
-{
-  const uint32_t count = in_tensors.size();
-
-  // Calculate
-  nnfw::cker::ConcatenationParams cker_param;
-  cker_param.axis = (int8_t)axis;
-  cker_param.inputs_count = count;
-
-  const auto out_shape = convertShape(out_tensor->tensorInfo().shape());
-
-  std::vector<nnfw::cker::Shape> in_shapes;
-  std::vector<const nnfw::cker::Shape *> in_shape_ptrs;
-  in_shapes.reserve(count);
-  in_shape_ptrs.reserve(count);
-  std::vector<const float *> in_ptrs;
-  for (uint32_t i = 0; i < count; i++)
-  {
-    in_shapes.push_back(convertShape(in_tensors[i]->tensorInfo().shape()));
-    in_shape_ptrs.push_back(&in_shapes[i]);
-    in_ptrs.push_back(reinterpret_cast<const float *>(in_tensors[i]->bufferRO()));
-  }
-
-  auto out_buffer = out_tensor->buffer();
-  float *out_ptr = reinterpret_cast<float *>(out_buffer);
-
-  nnfw::cker::Concatenation<float>(cker_param, in_shape_ptrs.data(), in_ptrs.data(), out_shape,
-                                   out_ptr);
-}
-
-void invokeConcat(const ExecEnv *env, const ir::Operation &node)
-{
-  const auto &concat_node = nnfw::misc::polymorphic_downcast<const ir::operation::Concat &>(node);
-  const int32_t axis_raw = concat_node.param().axis;
-
-  std::vector<const ITensor *> in_tensors;
-  for (const auto &e : concat_node.getInputs())
-  {
-    in_tensors.emplace_back(env->tensorAt(e));
-  }
-
-  const auto out_index = node.getOutputs().at(0);
-  const auto out_tensor = env->tensorAt(out_index);
-  const uint32_t axis = (axis_raw < 0) ? (axis_raw + out_tensor->num_dimensions()) : axis_raw;
-
-  const auto data_type = in_tensors[0]->data_type();
-  if (data_type == ir::DataType::FLOAT32)
-  {
-    invoke(in_tensors, out_tensor, axis);
-  }
-  else
-  {
-    throw std::runtime_error{"NYI: Support float32 only"};
-  }
-}
-} // namespace concat
-
-OpKernel *getConcat()
-{
-  static OpKernel kernel = {concat::prepareConcat, concat::invokeConcat};
-  return &kernel;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun