| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "TransposeConvolution2d.hpp" |
| |
| #include <armnnUtils/DataLayoutIndexed.hpp> |
| |
| namespace armnn |
| { |
| |
| using namespace armnnUtils; |
| |
| 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 channelsIndex = dataLayoutIndexed.GetChannelsIndex(); |
| const unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex(); |
| const unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex(); |
| |
| const unsigned int numBatches = inputShape[0]; |
| |
| const unsigned int inputWidth = inputShape[widthIndex]; |
| const unsigned int inputHeight = inputShape[heightIndex]; |
| const unsigned int inputDepth = inputShape[channelsIndex]; |
| |
| const unsigned int weightsHeight = weightsShape[heightIndex]; |
| const unsigned int weightsWidth = weightsShape[widthIndex]; |
| const unsigned int weightsDepth = weightsShape[channelsIndex]; |
| |
| const unsigned int outputHeight = outputShape[heightIndex]; |
| const unsigned int outputWidth = outputShape[widthIndex]; |
| const unsigned int outputDepth = outputShape[channelsIndex]; |
| |
| const unsigned int paddingLeft = descriptor.m_PadLeft; |
| const unsigned int paddingTop = descriptor.m_PadTop; |
| |
| const unsigned int strideX = descriptor.m_StrideX; |
| const unsigned int strideY = descriptor.m_StrideY; |
| |
| std::vector<float> outputBuffer(outputShape.GetNumElements(), 0); |
| |
| const std::vector<float> inputVec = inputDecoder.DecodeTensor(inputShape); |
| const std::vector<float> filterVec = weightsDecoder.DecodeTensor(weightsShape); |
| |
| for (unsigned int batch = 0u; batch < numBatches; ++batch) |
| { |
| for (unsigned int yInput = 0u; yInput < inputHeight; ++yInput) |
| { |
| for (unsigned int xInput = 0u; xInput < inputWidth; ++xInput) |
| { |
| unsigned int xOutputOrigin = xInput * strideX - paddingLeft; |
| unsigned int yOutputOrigin = yInput * strideY - paddingTop; |
| |
| for (unsigned int dOutput = 0u; dOutput < outputDepth; ++dOutput) |
| { |
| for (unsigned int yWeights = 0u; yWeights < weightsHeight; ++yWeights) |
| { |
| for (unsigned int xWeights = 0u; xWeights < weightsWidth; ++xWeights) |
| { |
| unsigned int yOutput = yOutputOrigin + yWeights; |
| unsigned int xOutput = xOutputOrigin + xWeights; |
| |
| if (yOutput < outputHeight && xOutput< outputWidth) |
| { |
| for (unsigned int dInput = 0u; dInput < inputDepth; dInput++) |
| { |
| unsigned int inputIndex; |
| unsigned int outputIndex; |
| unsigned int weightsIndex; |
| |
| if(descriptor.m_DataLayout == armnn::DataLayout::NHWC) |
| { |
| inputIndex = batch * inputHeight * inputWidth * inputDepth + |
| yInput * inputWidth * inputDepth + |
| xInput * inputDepth + |
| dInput; |
| |
| weightsIndex = dOutput * weightsHeight * weightsWidth * weightsDepth + |
| yWeights * weightsWidth * weightsDepth + |
| xWeights * weightsDepth + |
| dInput; |
| |
| outputIndex = batch * outputHeight * outputWidth * outputDepth + |
| yOutput * outputWidth * outputDepth + |
| xOutput * outputDepth + |
| dOutput; |
| } |
| else |
| { |
| inputIndex = batch * inputDepth * inputHeight * inputWidth + |
| dInput * inputHeight * inputWidth + |
| yInput * inputWidth + |
| xInput; |
| |
| weightsIndex = dOutput * weightsDepth * weightsHeight * weightsWidth + |
| dInput * weightsHeight * weightsWidth + |
| yWeights * weightsWidth + |
| xWeights; |
| |
| outputIndex = batch * outputDepth * outputHeight * outputWidth + |
| dOutput * outputHeight * outputWidth + |
| yOutput * outputWidth + |
| xOutput; |
| } |
| |
| outputBuffer[outputIndex] += inputVec[inputIndex] * filterVec[weightsIndex]; |
| } |
| } |
| } |
| } |
| |
| } |
| } |
| } |
| } |
| |
| // Apply bias (if enabled) |
| if (descriptor.m_BiasEnabled) |
| { |
| outputEncoder[0]; |
| Decoder<float>& rBiasesDecoder = *biasesDecoder; |
| |
| for (unsigned int batch = 0u; batch < numBatches; ++batch) |
| { |
| for (unsigned int dOutput = 0u; dOutput < outputDepth; ++dOutput) |
| { |
| rBiasesDecoder[dOutput]; |
| for (unsigned int yOutput = 0u; yOutput < outputHeight; ++yOutput) |
| { |
| for (unsigned int xOutput = 0u; xOutput < outputWidth; ++xOutput) |
| { |
| const unsigned int outputIndex = |
| dataLayoutIndexed.GetIndex(outputShape, batch, dOutput, yOutput, xOutput); |
| outputBuffer[outputIndex] += rBiasesDecoder.Get(); |
| } |
| } |
| } |
| } |
| } |
| outputEncoder[0]; |
| for (float output : outputBuffer) |
| { |
| outputEncoder.Set(output); |
| ++outputEncoder; |
| } |
| } |
| |
| } // namespace armnn |