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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 "AveragePool.h"
#include "ONNXHelpers.h"
#include "AttributeHelpers.h"
#include "ConvPoolHelpers.h"
#include "mir/ops/AvgPool2DOp.h"
namespace mir_onnx
{
void convertAveragePoolV1(const onnx::NodeProto &onnx_node, ConverterContext *context)
{
std::vector<mir::Operation::Output *> inputs = context->getNodeInputs(onnx_node);
mir::Graph *graph = context->getGraph();
assert(inputs.size() == 1);
auto input = inputs[0];
const auto &input_shape = input->getShape();
if (input_shape.rank() != 4)
throw std::runtime_error("AveragePool: only 2-D input is supported.");
constexpr int num_spatial_dims = 2;
const auto strides =
getAttributeValue(onnx_node, "strides", std::vector<std::int32_t>(num_spatial_dims, 1));
if (strides.size() != num_spatial_dims)
throw std::runtime_error("AveragePool: attribute 'strides' has incorrect size.");
const auto kernel_shape = getAttributeValue<std::vector<std::int32_t>>(onnx_node, "kernel_shape");
if (kernel_shape.size() != num_spatial_dims)
throw std::runtime_error("AveragePool: attribute 'kernel_shape' has incorrect size.");
std::vector<std::int32_t> padding_before(num_spatial_dims, 0);
std::vector<std::int32_t> padding_after(num_spatial_dims, 0);
if (const auto *pads_attr = findAttribute(onnx_node, "pads"))
{
const auto pads = getAttributeValue<std::vector<std::int32_t>>(*pads_attr);
if (pads.size() != num_spatial_dims * 2)
throw std::runtime_error("AveragePool: attribute 'pads' has incorrect size.");
padding_before.assign(pads.cbegin(), std::next(pads.cbegin(), num_spatial_dims));
padding_after.assign(std::next(pads.cbegin(), num_spatial_dims), pads.cend());
}
else
{
const auto auto_pad = getAttributeValue<std::string>(onnx_node, "auto_pad", "NOTSET");
const std::vector<std::int32_t> dilations(num_spatial_dims, 1);
inferAutoPadding(auto_pad, input_shape, dilations, strides, kernel_shape, padding_before,
padding_after);
}
mir::AvgPool2DOpAttributes attributes;
attributes.window = kernel_shape;
attributes.strides = strides;
attributes.padding_before = padding_before;
attributes.padding_after = padding_after;
attributes.include_pad = false;
attributes.data_format = mir::DataFormat::NCHW;
auto result = createOp<mir::ops::AvgPool2DOp>(graph, input, attributes)->getOutput(0);
context->setNodeOutputs(onnx_node, {result});
}
void convertAveragePoolV7(const onnx::NodeProto &onnx_node, ConverterContext *context)
{
const auto count_include_pad = getAttributeValue<int64_t>(onnx_node, "count_include_pad", 0);
if (count_include_pad != 0)
throw std::runtime_error("Not supported count_include_pad attribute!");
convertAveragePoolV1(onnx_node, context);
}
void convertAveragePoolV10(const onnx::NodeProto &onnx_node, ConverterContext *context)
{
const auto ceil_mode = getAttributeValue<int64_t>(onnx_node, "ceil_mode", 0);
if (ceil_mode != 0)
throw std::runtime_error("Not supported ceil_mode attribute!");
convertAveragePoolV7(onnx_node, context);
}
} // namespace mir_onnx
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