1 files changed, 0 insertions, 136 deletions
diff --git a/runtime/onert/core/src/interp/operations/FullyConnected.cc b/runtime/onert/core/src/interp/operations/FullyConnected.cc
deleted file mode 100644
index 12f529dab..000000000
--- a/runtime/onert/core/src/interp/operations/FullyConnected.cc
+++ /dev/null
@@ -1,136 +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/FullyConnected.h>
-
-#include "OperationUtil.h"
-
-#include "interp/Registration.h"
-#include "ir/operation/FullyConnected.h"
-#include "misc/polymorphic_downcast.h"
-
-namespace onert
-{
-namespace interp
-{
-namespace fc
-{
-
-void prepareFC(ExecEnv *env, const ir::Operation &node)
-{
-  const auto in_index = node.getInputs().at(ir::operation::FullyConnected::INPUT);
-  const auto kernel_index = node.getInputs().at(ir::operation::FullyConnected::WEIGHT);
-  const auto bias_index = node.getInputs().at(ir::operation::FullyConnected::BIAS);
-  const auto out_index = node.getOutputs().at(0);
-
-  const auto in_tensor = env->tensorAt(in_index);
-  const auto kernel_tensor = env->tensorAt(kernel_index);
-  const auto bias_tensor = env->tensorAt(bias_index);
-
-  UNUSED_RELEASE(in_tensor);
-  UNUSED_RELEASE(kernel_tensor);
-  UNUSED_RELEASE(bias_tensor);
-
-  assert(in_tensor->num_dimensions() >= 2);
-  assert(kernel_tensor->num_dimensions() == 2);
-  assert(bias_tensor->num_dimensions() == 1);
-
-  const auto input_size_with_batch = in_tensor->num_elements();
-  const auto num_units = kernel_tensor->dimension(0);
-  const auto input_size = kernel_tensor->dimension(1);
-  const auto batch_size = input_size_with_batch / input_size;
-  assert(input_size_with_batch % input_size == 0);
-  assert(num_units == bias_tensor->dimension(0));
-
-  // Make output tensor info
-  ir::Shape output_shape(2);
-  output_shape.dim(0) = batch_size;
-  output_shape.dim(1) = num_units;
-  const auto out_info =
-      ir::OperandInfo::createStaticInfo(output_shape, in_tensor->tensorInfo().typeInfo());
-  env->allocateIfNeeded(out_index, out_info);
-
-  auto out_tensor = env->tensorAt(out_index);
-  UNUSED_RELEASE(out_tensor);
-
-  // Handle same ifm & ofm data type only
-  assert(in_tensor->data_type() == out_tensor->data_type());
-  assert(out_tensor->num_dimensions() == 2);
-  assert(out_tensor->dimension(0) == batch_size);
-  assert(out_tensor->dimension(1) == num_units);
-}
-
-void invoke(const ITensor *ifm_tensor, const ITensor *ker_tensor, const ITensor *bias_tensor,
-            const ITensor *ofm_tensor, const ir::operation::FullyConnected::Param &param)
-{
-  const auto ifm_buffer = ifm_tensor->bufferRO();
-  const auto ker_buffer = ker_tensor->bufferRO();
-  const auto bias_buffer = bias_tensor->bufferRO();
-  auto ofm_buffer = ofm_tensor->buffer();
-
-  // Calculate
-  nnfw::cker::FullyConnectedParams cker_param;
-  cker_param.activation = convertActivationType(param.activation);
-  calculateActivationRange(param.activation, &cker_param.float_activation_min,
-                           &cker_param.float_activation_max);
-  const auto cker_ifm_shape = convertShape(ifm_tensor->tensorInfo().shape());
-  const auto cker_ker_shape = convertShape(ker_tensor->tensorInfo().shape());
-  const auto cker_bias_shape = convertShape(bias_tensor->tensorInfo().shape());
-  const auto cker_ofm_shape = convertShape(ofm_tensor->tensorInfo().shape());
-  const float *ifm_ptr = reinterpret_cast<const float *>(ifm_buffer);
-  const float *ker_ptr = reinterpret_cast<const float *>(ker_buffer);
-  const float *bias_ptr = reinterpret_cast<const float *>(bias_buffer);
-  float *ofm_ptr = reinterpret_cast<float *>(ofm_buffer);
-
-  nnfw::cker::FullyConnected(cker_param, cker_ifm_shape, ifm_ptr, cker_ker_shape, ker_ptr,
-                             cker_bias_shape, bias_ptr, cker_ofm_shape, ofm_ptr);
-}
-
-void invokeFC(const ExecEnv *env, const ir::Operation &node)
-{
-  const auto &conv_node =
-      nnfw::misc::polymorphic_downcast<const ir::operation::FullyConnected &>(node);
-
-  const auto ifm_index = node.getInputs().at(ir::operation::FullyConnected::INPUT);
-  const auto ker_index = node.getInputs().at(ir::operation::FullyConnected::WEIGHT);
-  const auto bias_index = node.getInputs().at(ir::operation::FullyConnected::BIAS);
-  const auto ofm_index = node.getOutputs().at(0);
-
-  const auto ifm_tensor = env->tensorAt(ifm_index);
-  const auto ker_tensor = env->tensorAt(ker_index);
-  const auto bias_tensor = env->tensorAt(bias_index);
-  const auto ofm_tensor = env->tensorAt(ofm_index);
-
-  const auto data_type = ifm_tensor->data_type();
-  if (data_type == ir::DataType::FLOAT32)
-  {
-    invoke(ifm_tensor, ker_tensor, bias_tensor, ofm_tensor, conv_node.param());
-  }
-  else
-  {
-    throw std::runtime_error{"NYI: Support float only"};
-  }
-}
-} // namespace fc
-
-OpKernel *getFullyConnected()
-{
-  static OpKernel kernel = {fc::prepareFC, fc::invokeFC};
-  return &kernel;
-}
-
-} // namespace interp
-} // namespace onert