1 files changed, 0 insertions, 152 deletions
diff --git a/runtime/neurun/core/src/exec/interp/operations/Conv2D.cc b/runtime/neurun/core/src/exec/interp/operations/Conv2D.cc
deleted file mode 100644
index 5242247a4..000000000
--- a/runtime/neurun/core/src/exec/interp/operations/Conv2D.cc
+++ /dev/null
@@ -1,152 +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/Conv.h>
-
-#include "OperationUtil.h"
-
-#include "exec/interp/Registration.h"
-#include "ir/operation/Conv2D.h"
-#include "util/Utils.h"
-#include "util/Padding.h"
-#include "util/ShapeInference.h"
-#include "misc/polymorphic_downcast.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-namespace conv2d
-{
-
-void prepareConv2D(ExecEnv *env, const ir::Operation &node)
-{
-  const auto in_index = node.getInputs().at(ir::operation::Conv2D::INPUT);
-  const auto kernel_index = node.getInputs().at(ir::operation::Conv2D::KERNEL);
-  const auto bias_index = node.getInputs().at(ir::operation::Conv2D::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);
-
-  assert(in_tensor->num_dimensions() == 4);
-  assert(kernel_tensor->num_dimensions() == 4);
-  assert(bias_tensor->num_dimensions() == 1);
-
-  UNUSED_RELEASE(in_tensor);
-  UNUSED_RELEASE(kernel_tensor);
-  UNUSED_RELEASE(bias_tensor);
-
-  const auto output_info = env->graph().operands().at(out_index).info();
-  if (output_info.total_size() == 0)
-  {
-    // Handle unspecified output shape
-    const auto &conv_node = nnfw::misc::polymorphic_downcast<const ir::operation::Conv2D &>(node);
-    const auto infered_output_shapes = shape_inference::inferConv2DShape(
-        in_tensor->tensorInfo().shape(), kernel_tensor->tensorInfo().shape(), conv_node.param());
-    env->allocateIfNeeded(out_index, {infered_output_shapes[0], output_info.typeInfo()});
-  }
-  else
-  {
-    env->allocateIfNeeded(out_index, output_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() == 4);
-}
-
-void invoke(const ITensor *ifm_tensor, const ITensor *ker_tensor, const ITensor *bias_tensor,
-            const ITensor *ofm_tensor, const ir::operation::Conv2D::Param &param)
-{
-  // TODO Support NCHW frontned
-  const auto ifm_shape = ifm_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC);
-  const auto ofm_shape = ofm_tensor->tensorInfo().shape().asFeature(ir::Layout::NHWC);
-  // Kernel format is [depth_out, kernel_height, kernel_width, depth_in].
-  const auto &ker_shape = ker_tensor->tensorInfo().shape();
-  const auto ker_height = ker_shape.dim(1);
-  const auto ker_width = ker_shape.dim(2);
-  const auto padding = neurun::util::calculatePadding(param.padding, ifm_shape, ofm_shape,
-                                                      param.stride, ker_width, ker_height);
-
-  // Calculate
-  float activation_min, activation_max;
-  calculateActivationRange(param.activation, &activation_min, &activation_max);
-
-  nnfw::cker::ConvParams cker_param;
-  cker_param.padding_values.width = padding.left;
-  cker_param.padding_values.height = padding.top;
-  cker_param.stride_width = param.stride.horizontal;
-  cker_param.stride_height = param.stride.vertical;
-  cker_param.dilation_width_factor = 1;
-  cker_param.dilation_height_factor = 1;
-  cker_param.float_activation_min = activation_min;
-  cker_param.float_activation_max = 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_tensor->bufferRO());
-  const float *ker_ptr = reinterpret_cast<const float *>(ker_tensor->bufferRO());
-  const float *bias_ptr = reinterpret_cast<const float *>(bias_tensor->bufferRO());
-  float *ofm_ptr = reinterpret_cast<float *>(ofm_tensor->buffer());
-
-  nnfw::cker::Conv(cker_param, cker_ifm_shape, ifm_ptr, cker_ker_shape, ker_ptr, cker_bias_shape,
-                   bias_ptr, cker_ofm_shape, ofm_ptr);
-}
-
-void invokeConv2D(const ExecEnv *env, const ir::Operation &node)
-{
-  const auto &conv_node = nnfw::misc::polymorphic_downcast<const ir::operation::Conv2D &>(node);
-
-  const auto ifm_index = node.getInputs().at(ir::operation::Conv2D::INPUT);
-  const auto ker_index = node.getInputs().at(ir::operation::Conv2D::KERNEL);
-  const auto bias_index = node.getInputs().at(ir::operation::Conv2D::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 float32 only"};
-  }
-}
-} // namespace conv2d
-
-OpKernel *getConv2D()
-{
-  static OpKernel kernel = {conv2d::prepareConv2D, conv2d::invokeConv2D};
-  return &kernel;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun