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/*
* 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 ¶m)
{
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
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