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// Copyright (C) 2018 Intel Corporation
//
// SPDX-License-Identifier: Apache-2.0
//
#pragma once
#include <description_buffer.hpp>
#include "ie_built_in_impl.hpp"
#include <map>
#include <memory>
#include <string>
#include <vector>
#include <debug.h>
#include <cmath>
#include <v2_format_parser.h>
namespace InferenceEngine {
namespace ShapeInfer {
/**
*@brief Implementation of Shape inference for Convolution layer
*/
class ConvShapeProp : public BuiltInShapeInferImpl {
public:
explicit ConvShapeProp(const std::string& type) : BuiltInShapeInferImpl(type) {}
void inferShapesImpl(const std::vector<SizeVector>& inShapes,
const std::map<std::string, std::string>& params,
const std::map<std::string, Blob::Ptr>& blobs,
std::vector<SizeVector>& outShapes) override {
LayerParams lp{};
ConvolutionLayer convLayer(lp);
convLayer.params = params;
convLayer.type = _type;
validate(&convLayer, inShapes, params, blobs);
float OH_temp, OW_temp;
auto dims = inShapes[0];
size_t inputN = dims[0];
size_t IH = dims[2];
size_t IW = dims[3];
size_t KH = 0, KW = 0;
int PR = -1, PB = -1;
if (convLayer._dilation[Y_AXIS])
KH = (convLayer._kernel[Y_AXIS] - 1) * convLayer._dilation[Y_AXIS] + 1;
else
KH = convLayer._kernel[Y_AXIS];
if (convLayer._dilation[X_AXIS])
KW = (convLayer._kernel[X_AXIS] - 1) * convLayer._dilation[X_AXIS] + 1;
else
KW = convLayer._kernel[X_AXIS];
size_t SH = convLayer._stride[Y_AXIS];
size_t SW = convLayer._stride[X_AXIS];
size_t PH = convLayer._padding[Y_AXIS];
size_t PW = convLayer._padding[X_AXIS];
size_t OC = convLayer._out_depth;
auto it = convLayer.params.find("auto_pad");
std::string padType;
if (it != convLayer.params.end()) padType = it->second;
if (padType == "valid") {
OH_temp = std::ceil((IH - KH + 1.f) / SH);
OW_temp = std::ceil((IW - KW + 1.f) / SW);
} else if (padType == "same_upper") {
OH_temp = std::ceil(1.f * IH / SH);
OW_temp = std::ceil(1.f * IW / SW);
} else if (padType == "same_lower") {
OH_temp = std::floor(1.f * IH / SH);
OW_temp = std::floor(1.f * IW / SW);
} else {
auto ir_version = details::BaseCreator::version_;
bool isEndPaddingsSet = false;
try {
if (ir_version == 3) {
auto pads_end = convLayer.GetParamAsUInts("pads_end");
PR = pads_end[pads_end.size() - 1 - X_AXIS];
PB = pads_end[pads_end.size() - 1 - Y_AXIS];
} else if (ir_version < 3) {
PR = convLayer.GetParamAsInt("pad-r");
PB = convLayer.GetParamAsInt("pad-b");
}
isEndPaddingsSet = true;
} catch (...) {}
if (!isEndPaddingsSet) {
OH_temp = std::floor((IH + 2.f * PH - KH) / SH) + 1.f;
OW_temp = std::floor((IW + 2.f * PW - KW) / SW) + 1.f;
} else {
OH_temp = std::floor(1.f * (IH + PH + PB - KH) / SH) + 1.f;
OW_temp = std::floor(1.f * (IW + PW + PR - KW) / SW) + 1.f;
}
}
if (OH_temp < 0 || OW_temp < 0)
THROW_IE_EXCEPTION << "New shapes " << details::dumpVec(dims) << " make output shape negative";
size_t OH = static_cast<size_t>(OH_temp);
size_t OW = static_cast<size_t>(OW_temp);
outShapes.push_back({inputN, OC, OH, OW});
}
};
} // namespace ShapeInfer
} // namespace InferenceEngine
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