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Diffstat (limited to 'src/backends/reference/workloads/TransposeConvolution2d.cpp')
| -rw-r--r-- | src/backends/reference/workloads/TransposeConvolution2d.cpp | 248 |
1 files changed, 248 insertions, 0 deletions
diff --git a/src/backends/reference/workloads/TransposeConvolution2d.cpp b/src/backends/reference/workloads/TransposeConvolution2d.cpp new file mode 100644 index 0000000000..db15cefe10 --- /dev/null +++ b/src/backends/reference/workloads/TransposeConvolution2d.cpp @@ -0,0 +1,248 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "TransposeConvolution2d.hpp" + +#include <DataLayoutIndexed.hpp> + +namespace armnn +{ + +using namespace armnnUtils; + +struct TensorData +{ + TensorShape shape; + std::vector<float> data; +}; + +TensorData SetUpStridedInput(const TensorShape& inputShape, + Decoder<float>& inputDecoder, + const TransposeConvolution2dDescriptor& descriptor, + const DataLayoutIndexed& dataLayoutIndexed) +{ + const unsigned int cIndex = dataLayoutIndexed.GetChannelsIndex(); + const unsigned int hIndex = dataLayoutIndexed.GetHeightIndex(); + const unsigned int wIndex = dataLayoutIndexed.GetWidthIndex(); + + const unsigned int batches = inputShape[0]; + const unsigned int channels = inputShape[cIndex]; + + const unsigned int wInput = inputShape[wIndex]; + const unsigned int hInput = inputShape[hIndex]; + + const unsigned int wStridedInput = 1u + descriptor.m_StrideX * (wInput - 1); + const unsigned int hStridedInput = 1u + descriptor.m_StrideY * (hInput - 1); + + TensorData stridedInput; + stridedInput.data = std::vector<float>(batches * channels * wStridedInput * hStridedInput, 0.0f); + stridedInput.shape = TensorShape(4); + + stridedInput.shape[0] = batches; + stridedInput.shape[cIndex] = channels; + stridedInput.shape[hIndex] = hStridedInput; + stridedInput.shape[wIndex] = wStridedInput; + + // expand input data with strides + for (unsigned int batchIdx = 0u; batchIdx < batches; ++batchIdx) + { + for (unsigned int cInput = 0u; cInput < channels; ++cInput) + { + for (unsigned int yInput = 0u, yStrided = 0u; + yInput < hInput && yStrided < hStridedInput; + ++yInput, yStrided += descriptor.m_StrideY) + { + for (unsigned int xInput = 0u, xStrided = 0u; + xInput < wInput && xStrided < wStridedInput; + ++xInput, xStrided += descriptor.m_StrideX) + { + unsigned int inputIdx = + dataLayoutIndexed.GetIndex(inputShape, batchIdx, cInput, yInput, xInput); + unsigned int stridedInputIdx = + dataLayoutIndexed.GetIndex(stridedInput.shape, batchIdx, cInput, yStrided, xStrided); + + inputDecoder[inputIdx]; + stridedInput.data[stridedInputIdx] = inputDecoder.Get(); + } + } + } + } + + return stridedInput; +} + +TensorData SetUpEmptyPaddedOutput(const TensorShape& outputShape, + const TransposeConvolution2dDescriptor& descriptor, + const DataLayoutIndexed& dataLayoutIndexed) +{ + const unsigned int cIndex = dataLayoutIndexed.GetChannelsIndex(); + const unsigned int hIndex = dataLayoutIndexed.GetHeightIndex(); + const unsigned int wIndex = dataLayoutIndexed.GetWidthIndex(); + + const unsigned int batches = outputShape[0]; + const unsigned int channels = outputShape[cIndex]; + + const unsigned int wOutput = outputShape[wIndex]; + const unsigned int hOutput = outputShape[hIndex]; + + const unsigned int wPaddedOutput = wOutput + descriptor.m_PadLeft + descriptor.m_PadRight; + const unsigned int hPaddedOutput = hOutput + descriptor.m_PadTop + descriptor.m_PadBottom; + + TensorData paddedOutput; + paddedOutput.data = std::vector<float>(batches * channels * wPaddedOutput * hPaddedOutput, 0.0f); + paddedOutput.shape = TensorShape(4); + + paddedOutput.shape[0] = batches; + paddedOutput.shape[cIndex] = channels; + paddedOutput.shape[hIndex] = hPaddedOutput; + paddedOutput.shape[wIndex] = wPaddedOutput; + + return paddedOutput; +} + +void Deconvolve(const TensorData& stridedInput, + TensorData& paddedOutput, + const TensorShape& weightsShape, + Decoder<float>& weightsDecoder, + const DataLayoutIndexed& dataLayoutIndexed) +{ + const unsigned int cIndex = dataLayoutIndexed.GetChannelsIndex(); + const unsigned int hIndex = dataLayoutIndexed.GetHeightIndex(); + const unsigned int wIndex = dataLayoutIndexed.GetWidthIndex(); + + const unsigned int batches = stridedInput.shape[0]; + const unsigned int channels = stridedInput.shape[cIndex]; + + const unsigned int wKernel = weightsShape[wIndex]; + const unsigned int hKernel = weightsShape[hIndex]; + + const unsigned int wStridedInput = stridedInput.shape[wIndex]; + const unsigned int hStridedInput = stridedInput.shape[hIndex]; + + // loop through all input elements + for (unsigned int batchIdx = 0u; batchIdx < batches; ++batchIdx) + { + for (unsigned int cInput = 0u; cInput < channels; ++cInput) + { + for (unsigned int yInput = 0u; yInput < hStridedInput; ++yInput) + { + for (unsigned int xInput = 0u; xInput < wStridedInput; ++xInput) + { + // obtain input value + unsigned int inputIdx = + dataLayoutIndexed.GetIndex(stridedInput.shape, batchIdx, cInput, yInput, xInput); + float inputValue = stridedInput.data[inputIdx]; + + // loop through kernel + for (unsigned int yKernel = 0u; yKernel < hKernel; ++yKernel) + { + for (unsigned int xKernel = 0; xKernel < wKernel; ++xKernel) + { + unsigned int kernelIdx = + dataLayoutIndexed.GetIndex(weightsShape, batchIdx, cInput, yKernel, xKernel); + + weightsDecoder[kernelIdx]; + float kernelValue = weightsDecoder.Get(); + + unsigned int xOutput = xInput + xKernel; + unsigned int yOutput = yInput + yKernel; + + // compute output increment + float outputValue = inputValue * kernelValue; + + unsigned int outputIdx = dataLayoutIndexed.GetIndex(paddedOutput.shape, + batchIdx, + cInput, + yOutput, + xOutput); + + // set output value + paddedOutput.data[outputIdx] += outputValue; + } + } + } + } + } + } +} + +void TransposeConvolution2dImpl(const TransposeConvolution2dDescriptor& descriptor, + const TensorShape& inputShape, + Decoder<float>& inputDecoder, + const TensorShape& outputShape, + Encoder<float>& outputEncoder, + const TensorShape& weightsShape, + Decoder<float>& weightsDecoder, + Decoder<float>* biasesDecoder) +{ + if (descriptor.m_BiasEnabled && !biasesDecoder) + { + throw InvalidArgumentException("Biases enabled but no bias data provided"); + } + + const DataLayoutIndexed dataLayoutIndexed(descriptor.m_DataLayout); + + const unsigned int cIndex = dataLayoutIndexed.GetChannelsIndex(); + const unsigned int hIndex = dataLayoutIndexed.GetHeightIndex(); + const unsigned int wIndex = dataLayoutIndexed.GetWidthIndex(); + + const unsigned int numBatches = inputShape[0]; + const unsigned int numChannels = inputShape[cIndex]; + + // set up temporary strided input + TensorData stridedInput = SetUpStridedInput(inputShape, inputDecoder, descriptor, dataLayoutIndexed); + + // set up temporary (empty) padded output + TensorData paddedOutput = SetUpEmptyPaddedOutput(outputShape, descriptor, dataLayoutIndexed); + + // run deconvolution (without biases) on strided input to produce padded output + Deconvolve(stridedInput, paddedOutput, weightsShape, weightsDecoder, dataLayoutIndexed); + + const unsigned int wPaddedOutput = paddedOutput.shape[wIndex]; + const unsigned int hPaddedOutput = paddedOutput.shape[hIndex]; + + // remove padding and apply bias (if enabled) + for (unsigned int batchIdx = 0u; batchIdx < numBatches; ++batchIdx) + { + for (unsigned int cOutput = 0u; cOutput < numChannels; ++cOutput) + { + // update bias decoder iterator + if (descriptor.m_BiasEnabled) + { + (*biasesDecoder)[cOutput]; + } + + for (unsigned int yPaddedOutput = descriptor.m_PadTop; + yPaddedOutput < (hPaddedOutput - descriptor.m_PadBottom); + ++yPaddedOutput) + { + for (unsigned int xPaddedOutput = descriptor.m_PadLeft; + xPaddedOutput < (wPaddedOutput - descriptor.m_PadRight); + ++xPaddedOutput) + { + unsigned int xOutput = xPaddedOutput - descriptor.m_PadLeft; + unsigned int yOutput = yPaddedOutput - descriptor.m_PadTop; + + unsigned int outputIdx = + dataLayoutIndexed.GetIndex(outputShape, batchIdx, cOutput, yOutput, xOutput); + unsigned int paddedOutputIdx = + dataLayoutIndexed.GetIndex(paddedOutput.shape, batchIdx, cOutput, yPaddedOutput, xPaddedOutput); + + // encode (copy) output data + outputEncoder[outputIdx]; + outputEncoder.Set(paddedOutput.data[paddedOutputIdx]); + + // apply bias (if enabled) + if (descriptor.m_BiasEnabled) + { + outputEncoder.Set(outputEncoder.Get() + biasesDecoder->Get()); + } + } + } + } + } +} + +} // namespace armnn
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