1 files changed, 0 insertions, 145 deletions
diff --git a/runtime/neurun/core/src/exec/interp/operations/TransposeConv.cc b/runtime/neurun/core/src/exec/interp/operations/TransposeConv.cc
deleted file mode 100644
index 70b72c88d..000000000
--- a/runtime/neurun/core/src/exec/interp/operations/TransposeConv.cc
+++ /dev/null
@@ -1,145 +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/TransposeConv.h>
-#include <misc/polymorphic_downcast.h>
-
-#include "OperationUtil.h"
-
-#include "exec/interp/Registration.h"
-#include "ir/operation/TransposeConv.h"
-#include "util/Padding.h"
-
-namespace neurun
-{
-namespace exec
-{
-namespace interp
-{
-namespace
-{
-
-void prepareTransposeConv(ExecEnv *env, const ir::Operation &node)
-{
-  const auto ifm_index = node.getInputs().at(ir::operation::TransposeConv::INPUT);
-  const auto ker_index = node.getInputs().at(ir::operation::TransposeConv::KERNEL);
-  const auto ofm_shape_index = node.getInputs().at(ir::operation::TransposeConv::OUTPUT_SHAPE);
-  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 ofm_shape_tensor = env->tensorAt(ofm_shape_index);
-
-  assert(ifm_tensor->num_dimensions() == 4);
-  assert(ker_tensor->num_dimensions() == 4);
-  assert(ofm_shape_tensor->num_dimensions() == 1);
-
-  UNUSED_RELEASE(ifm_tensor);
-  UNUSED_RELEASE(ker_tensor);
-  UNUSED_RELEASE(ofm_shape_tensor);
-
-  const auto output_info = env->graph().operands().at(ofm_index).info();
-  if (output_info.total_size() == 0)
-  {
-    // TODO: Handle unspecified output shape
-    throw std::runtime_error{"Interp(TConv): NYI unspecified output shape"};
-  }
-  else
-  {
-    env->allocateIfNeeded(ofm_index, output_info);
-  }
-
-  auto ofm_tensor = env->tensorAt(ofm_index);
-  UNUSED_RELEASE(ofm_tensor);
-
-  // Handle same ifm & ofm data type only
-  if (ifm_tensor->data_type() != ofm_tensor->data_type())
-  {
-    throw std::runtime_error{"Interp(TConv): Different I/O data dype"};
-  }
-
-  if (ofm_tensor->num_dimensions() != 4)
-  {
-    throw std::runtime_error{"Interp(TConv): Invalid output rank"};
-  }
-}
-
-void invoke(const ITensor *ifm_tensor, const ITensor *ker_tensor, const ITensor *ofm_tensor,
-            const ir::operation::TransposeConv::Param &param)
-{
-  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, ofm_shape, ifm_shape,
-                                                      param.stride, ker_width, ker_height);
-
-  nnfw::cker::TransposeConvParams 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;
-
-  const auto cker_ifm_shape = convertShape(ifm_tensor->tensorInfo().shape());
-  const auto cker_ker_shape = convertShape(ker_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());
-  float *ofm_ptr = reinterpret_cast<float *>(ofm_tensor->buffer());
-
-  nnfw::cker::TransposeConv(cker_param, cker_ifm_shape, ifm_ptr, cker_ker_shape, ker_ptr,
-                            cker_ofm_shape, ofm_ptr);
-}
-
-void invokeTransposeConv(const ExecEnv *env, const ir::Operation &node)
-{
-  const auto &tconv_node =
-      nnfw::misc::polymorphic_downcast<const ir::operation::TransposeConv &>(node);
-
-  const auto ifm_index = node.getInputs().at(ir::operation::TransposeConv::INPUT);
-  const auto ker_index = node.getInputs().at(ir::operation::TransposeConv::KERNEL);
-  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 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, ofm_tensor, tconv_node.param());
-  }
-  else
-  {
-    throw std::runtime_error{"Interp(TConv): Support float32 only"};
-  }
-}
-
-} // namespace transposeconv
-
-OpKernel *getTransposeConv()
-{
-  static OpKernel kernel = {prepareTransposeConv, invokeTransposeConv};
-  return &kernel;
-}
-
-} // namespace interp
-} // namespace exec
-} // namespace neurun