diff options
Diffstat (limited to 'runtime/neurun/core/src/exec/interp/operations/Pad.cc')
-rw-r--r-- | runtime/neurun/core/src/exec/interp/operations/Pad.cc | 109 |
1 files changed, 109 insertions, 0 deletions
diff --git a/runtime/neurun/core/src/exec/interp/operations/Pad.cc b/runtime/neurun/core/src/exec/interp/operations/Pad.cc new file mode 100644 index 000000000..0c8267a90 --- /dev/null +++ b/runtime/neurun/core/src/exec/interp/operations/Pad.cc @@ -0,0 +1,109 @@ +/* + * 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/Pad.h> + +#include "OperationUtil.h" + +#include "exec/interp/Registration.h" +#include "ir/operation/Pad.h" + +namespace neurun +{ +namespace exec +{ +namespace interp +{ +namespace +{ + +void preparePad(ExecEnv *env, const ir::Operation &node) +{ + const auto input_index = node.getInputs().at(ir::operation::Pad::INPUT); + const auto output_index = node.getOutputs().at(0); + + const auto input_tensor = env->tensorAt(input_index); + + const auto output_info = env->graph().operands().at(output_index).info(); + + // Check shape and type lhs is same with rhs + // TODO Util function to compare TensorInfo + if (output_info.total_size() == 0) + { + throw std::runtime_error{"Interp(Pad): NYI unspecified output shape"}; + } + else + { + env->allocateIfNeeded(output_index, output_info); + } + + const auto output_tensor = env->tensorAt(output_index); + if (input_tensor->data_type() != output_tensor->data_type()) + { + throw std::runtime_error{"Interp(Pad): Invalid output type"}; + } +} + +void invoke(const ITensor *input_tensor, const ITensor *pad_tensor, const ITensor *output_tensor) +{ + const auto input_buffer = input_tensor->bufferRO(); + const auto pad_buffer = pad_tensor->bufferRO(); + auto output_buffer = output_tensor->buffer(); + + int32_t pad_rank = pad_tensor->dimension(0); + + const auto cker_input_shape = convertShape(input_tensor->tensorInfo().shape()); + const auto cker_output_shape = convertShape(output_tensor->tensorInfo().shape()); + const float *input_ptr = reinterpret_cast<const float *>(input_buffer); + const int32_t *pad_ptr = reinterpret_cast<const int32_t *>(pad_buffer); + float *output_ptr = reinterpret_cast<float *>(output_buffer); + + nnfw::cker::Pad(pad_ptr, pad_rank, cker_input_shape, input_ptr, cker_output_shape, output_ptr, + nullptr); +} + +void invokePad(const ExecEnv *env, const ir::Operation &node) +{ + const auto input_index = node.getInputs().at(ir::operation::Pad::INPUT); + const auto pad_index = node.getInputs().at(ir::operation::Pad::PAD); + const auto output_index = node.getOutputs().at(0); + + const auto input_tensor = env->tensorAt(input_index); + const auto pad_tensor = env->tensorAt(pad_index); + const auto output_tensor = env->tensorAt(output_index); + + const auto data_type = input_tensor->data_type(); + + if (data_type == ir::DataType::FLOAT32) + { + invoke(input_tensor, pad_tensor, output_tensor); + } + else + { + throw std::runtime_error{"Interp(Pad): NYI - Unsupported data type"}; + } +} +} // namespace + +OpKernel *getPad() +{ + static OpKernel kernel = {preparePad, invokePad}; + return &kernel; +} + +} // namespace interp +} // namespace exec +} // namespace neurun |