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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/MaxPool.h>
#include "OperationUtil.h"
#include "exec/interp/Registration.h"
#include "model/operation/MaxPool2DNode.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
{
void prepareMaxPool2D(ExecEnv *env, const model::Operation &node)
{
const auto in_index = node.getInputs().at(0);
const auto out_index = node.getOutputs().at(0);
const auto in_tensor = env->tensorAt(in_index);
assert(in_tensor->num_dimensions() == 4);
UNUSED_RELEASE(in_tensor);
const auto output_info = env->model().operands.at(out_index).info();
if (output_info.total_size() == 0)
{
// Handle unspecified output shape
const auto &maxpool_node =
nnfw::misc::polymorphic_downcast<const model::operation::MaxPool2DNode &>(node);
const auto infered_output_shapes =
shape_inference::inferMaxPoolShape(in_tensor->tensorInfo().shape(), maxpool_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 *in_tensor, const ITensor *out_tensor,
const model::operation::MaxPool2DNode::Param ¶m)
{
// TODO support NCHW frontend
const auto ifm_shape = in_tensor->tensorInfo().shape().asFeature(model::Layout::NHWC);
const auto ofm_shape = out_tensor->tensorInfo().shape().asFeature(model::Layout::NHWC);
const auto padding = neurun::util::calculatePadding(param.padding, ifm_shape, ofm_shape,
param.stride, param.kw, param.kh);
// Calculate
nnfw::cker::MaxPoolParams cker_param;
calculateActivationRange(param.activation, &cker_param.float_activation_min,
&cker_param.float_activation_max);
cker_param.filter_width = param.kw;
cker_param.filter_height = param.kh;
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;
const auto in_shape = convertShape(in_tensor->tensorInfo().shape());
const auto out_shape = convertShape(out_tensor->tensorInfo().shape());
const float *in_ptr = reinterpret_cast<const float *>(in_tensor->bufferRO());
float *out_ptr = reinterpret_cast<float *>(out_tensor->buffer());
nnfw::cker::MaxPool(cker_param, in_shape, in_ptr, out_shape, out_ptr);
}
void invokeMaxPool2D(const ExecEnv *env, const model::Operation &node)
{
const auto &maxpool_node =
nnfw::misc::polymorphic_downcast<const model::operation::MaxPool2DNode &>(node);
const auto in_index = node.getInputs().at(0);
const auto out_index = node.getOutputs().at(0);
const auto in_tensor = env->tensorAt(in_index);
const auto out_tensor = env->tensorAt(out_index);
const auto data_type = in_tensor->data_type();
if (data_type == model::DataType::FLOAT32)
{
invoke(in_tensor, out_tensor, maxpool_node.param());
}
else
{
throw std::runtime_error{"NYI: Support float32 only"};
}
}
} // namespace
OpKernel *getMaxPool2DNode()
{
static OpKernel kernel = {prepareMaxPool2D, invokeMaxPool2D};
return &kernel;
}
} // namespace interp
} // namespace exec
} // namespace neurun
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