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//
// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "Conv3dImpl.hpp"
namespace armnn
{
void Convolve3d(const TensorShape& rInputShape,
Decoder<float>& rInputDecoder,
const TensorShape& rOutputShape,
Encoder<float>& rOutputEncoder,
const TensorShape& rFilterShape,
Decoder<float>& rFilterDecoder,
bool biasEnabled,
Decoder<float>* pBiasDecoder,
DataLayout dataLayout,
unsigned int paddingTop,
unsigned int paddingLeft,
unsigned int paddingFront,
unsigned int xStride,
unsigned int yStride,
unsigned int zStride,
unsigned int xDilation,
unsigned int yDilation,
unsigned int zDilation)
{
if (biasEnabled && !pBiasDecoder)
{
throw InvalidArgumentException("Bias is enabled but the bias data is invalid");
}
const armnnUtils::DataLayoutIndexed dataLayoutIndexed(dataLayout);
const unsigned int channelsIndex = dataLayoutIndexed.GetChannelsIndex();
const unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex();
const unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex();
const unsigned int depthIndex = dataLayoutIndexed.GetDepthIndex();
const unsigned int inChannels = rInputShape[channelsIndex];
const unsigned int outChannels = rOutputShape[channelsIndex];
const unsigned int batchSize = rOutputShape[0];
const unsigned int outputHeight = rOutputShape[heightIndex];
const unsigned int outputWidth = rOutputShape[widthIndex];
const unsigned int outputDepth = rOutputShape[depthIndex];
const unsigned int inputHeight = rInputShape[heightIndex];
const unsigned int inputWidth = rInputShape[widthIndex];
const unsigned int inputDepth = rInputShape[depthIndex];
// Conv3d weights layout: [D,H,W,I,O]
const unsigned int filterDepth = rFilterShape[0];
const unsigned int filterHeight = rFilterShape[1];
const unsigned int filterWidth = rFilterShape[2];
const std::vector<float> inputVec = rInputDecoder.DecodeTensor(rInputShape);
const std::vector<float> filterVec = rFilterDecoder.DecodeTensor(rFilterShape);
const TensorShape biasShape{outChannels};
const std::vector<float> biasVec = biasEnabled ? pBiasDecoder->DecodeTensor(biasShape) : std::vector<float>();
for (unsigned int batchIdx = 0; batchIdx < batchSize; batchIdx++)
{
for (unsigned int zOutput = 0; zOutput < outputDepth; zOutput++)
{
for (unsigned int xOutput = 0; xOutput < outputWidth; xOutput++)
{
for (unsigned int yOutput = 0; yOutput < outputHeight; yOutput++)
{
for (unsigned int cOutput = 0; cOutput < outChannels; cOutput++)
{
// This loop goes over each output element.
float sum = 0.0f;
// Loop over each input channel.
for (unsigned int zFilter = 0; zFilter < filterDepth; zFilter++)
{
for (unsigned int yFilter = 0; yFilter < filterHeight; yFilter++)
{
for (unsigned int xFilter = 0; xFilter < filterWidth; xFilter++)
{
for (unsigned int cInput = 0; cInput < inChannels; cInput++)
{
// This loop goes over each input element for each output element.
unsigned int filterIndex = 0;
// Conv3d weights layout: [D,H,W,I,O]
// Keep this implementation, as using DataLayoutIndexed::GetIndex
// causes large performance regression.
filterIndex = zFilter * filterHeight * filterWidth * inChannels * outChannels +
yFilter * filterWidth * inChannels * outChannels +
xFilter * inChannels * outChannels +
cInput * outChannels +
cOutput;
unsigned int yInput = yOutput * yStride + yFilter * yDilation;
unsigned int xInput = xOutput * xStride + xFilter * xDilation;
unsigned int zInput = zOutput * zStride + zFilter * zDilation;
float inputValue;
// Check if we're in the padding.
if (yInput < paddingTop || yInput >= inputHeight + paddingTop ||
xInput < paddingLeft || xInput >= inputWidth + paddingLeft ||
zInput < paddingFront || zInput >= inputDepth + paddingFront)
{
inputValue = 0.0f;
}
else
{
unsigned int inputIndex = 0;
// Keep this implementation, as using DataLayoutIndexed::GetIndex
// causes large performance regression.
inputIndex = batchIdx * inputDepth * inputHeight * inputWidth * inChannels +
(zInput-paddingFront) * inputHeight * inputWidth * inChannels +
(yInput-paddingTop) * inputWidth * inChannels +
(xInput-paddingLeft) * inChannels +
cInput;
inputValue = inputVec[inputIndex];
}
sum += filterVec[filterIndex] * inputValue;
}
}
}
}
if (biasEnabled)
{
sum += biasVec[cOutput];
}
unsigned int outIdx = batchIdx * outputDepth * outputHeight * outputWidth * outChannels +
zOutput * outputHeight * outputWidth * outChannels +
yOutput * outputWidth * outChannels +
xOutput * outChannels +
cOutput;
rOutputEncoder[outIdx];
rOutputEncoder.Set(sum);
}
}
}
}
}
}
} // namespace armnn
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