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-rw-r--r--src/backends/reference/workloads/ConvImpl.hpp177
1 files changed, 0 insertions, 177 deletions
diff --git a/src/backends/reference/workloads/ConvImpl.hpp b/src/backends/reference/workloads/ConvImpl.hpp
index 3551ba8f90..7dba760d87 100644
--- a/src/backends/reference/workloads/ConvImpl.hpp
+++ b/src/backends/reference/workloads/ConvImpl.hpp
@@ -50,183 +50,6 @@ private:
int32_t m_RightShift;
};
-/// An implementation shared by normal and depthwise convolution.
-template<typename ConvData, typename InputType, typename BiasType, typename AccumulatorType>
-static void ConvImpl(ConvData data,
- const InputType* inputData,
- float inputScale,
- int32_t inputOffset,
- const InputType* filterData,
- float filterScale,
- int32_t filterOffset,
- const BiasType* biasData,
- float outputScale,
- int32_t outputOffset,
- const TensorInfo& filterInfo,
- bool depthwise = false)
-{
- if (data.m_Parameters.m_BiasEnabled && !biasData)
- {
- throw InvalidArgumentException("Bias is enabled but the bias data is invalid");
- }
-
- const TensorInfo& inputInfo = GetTensorInfo(data.m_Inputs[0]);
- const TensorInfo& outputInfo = GetTensorInfo(data.m_Outputs[0]);
-
- TensorBufferArrayView<InputType> output(outputInfo.GetShape(),
- GetOutputTensorData<InputType>(0, data),
- data.m_Parameters.m_DataLayout);
-
- const armnnUtils::DataLayoutIndexed dataLayoutIndexed(data.m_Parameters.m_DataLayout);
-
- const unsigned int channelsIndex = dataLayoutIndexed.GetChannelsIndex();
- const unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex();
- const unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex();
-
- unsigned int depthMultiplier = depthwise ? filterInfo.GetShape()[0] : 1;
- unsigned int inputChannels = depthwise ? filterInfo.GetShape()[1] : filterInfo.GetShape()[channelsIndex];
- unsigned int outputChannels = depthwise ? inputChannels * depthMultiplier : filterInfo.GetShape()[0];
-
- unsigned int batchSize = outputInfo.GetShape()[0];
- unsigned int outputHeight = outputInfo.GetShape()[heightIndex];
- unsigned int outputWidth = outputInfo.GetShape()[widthIndex];
- unsigned int inputHeight = inputInfo.GetShape()[heightIndex];
- unsigned int inputWidth = inputInfo.GetShape()[widthIndex];
-
- unsigned int filterHeight = depthwise ? filterInfo.GetShape()[2] : filterInfo.GetShape()[heightIndex];
- unsigned int filterWidth = depthwise ? filterInfo.GetShape()[3] : filterInfo.GetShape()[widthIndex];
-
- unsigned int paddingTop = data.m_Parameters.m_PadTop;
- unsigned int paddingLeft = data.m_Parameters.m_PadLeft;
- unsigned int xStride = data.m_Parameters.m_StrideX;
- unsigned int yStride = data.m_Parameters.m_StrideY;
- unsigned int xDilation = data.m_Parameters.m_DilationX;
- unsigned int yDilation = data.m_Parameters.m_DilationY;
-
- // The world's least efficient convolution.
- for (unsigned int batchIdx = 0; batchIdx < batchSize; batchIdx++)
- {
- for (unsigned int cOutput = 0; cOutput < outputChannels; cOutput++)
- {
- for (unsigned int yOutput = 0; yOutput < outputHeight; yOutput++)
- {
- for (unsigned int xOutput = 0; xOutput < outputWidth; xOutput++)
- {
- // This loop goes over each output element.
- AccumulatorType sum = AccumulatorType();
-
- // For depthwise, each output channel corresponds to exactly one input channel.
- // For normal, must loop over each input channel.
- for (unsigned int cInput = 0; cInput < (depthwise ? 1 : inputChannels); cInput++)
- {
- unsigned int depthwiseMultiplierIdx = 0;
- if (depthwise)
- {
- cInput = cOutput / depthMultiplier;
- depthwiseMultiplierIdx = cOutput % depthMultiplier;
- }
-
- for (unsigned int yFilter = 0; yFilter < filterHeight; yFilter++)
- {
- for (unsigned int xFilter = 0; xFilter < filterWidth; xFilter++)
- {
- // This loop goes over each input element for each output element.
-
- unsigned int filterIndex = 0;
-
- // Since dimensionality of kernel depends on depthwiseness, so does index.
- if (depthwise)
- {
- filterIndex = depthwiseMultiplierIdx * filterWidth * filterHeight * inputChannels +
- cInput * filterWidth * filterHeight +
- yFilter * filterWidth +
- xFilter;
- }
- else
- {
- if (data.m_Parameters.m_DataLayout == DataLayout::NHWC)
- {
- filterIndex = cOutput * filterHeight * filterWidth * inputChannels +
- yFilter * filterWidth * inputChannels +
- xFilter * inputChannels +
- cInput;
- }
- else
- {
- filterIndex = cOutput * filterWidth * filterHeight * inputChannels +
- cInput * filterWidth * filterHeight +
- yFilter * filterWidth +
- xFilter;
- }
- }
-
- AccumulatorType filterValue = filterData[filterIndex] -
- boost::numeric_cast<AccumulatorType>(filterOffset);
-
- unsigned int yInput = yOutput * yStride + yFilter * yDilation;
- unsigned int xInput = xOutput * xStride + xFilter * xDilation;
-
- AccumulatorType inputValue;
-
- // Check if we're in the padding.
- if (yInput < paddingTop || yInput >= inputHeight + paddingTop ||
- xInput < paddingLeft || xInput >= inputWidth + paddingLeft )
- {
- inputValue = AccumulatorType();
- }
- else
- {
- unsigned int inputIndex;
-
- if (data.m_Parameters.m_DataLayout == DataLayout::NHWC)
- {
- inputIndex = batchIdx * inputHeight * inputWidth * inputChannels +
- (yInput - paddingTop) * inputWidth * inputChannels +
- (xInput - paddingLeft) * inputChannels +
- cInput;
-
- }
- else
- {
- inputIndex = batchIdx * inputWidth * inputHeight * inputChannels +
- inputWidth * inputHeight * cInput +
- inputWidth * (yInput - paddingTop) +
- xInput - paddingLeft;
- }
-
- inputValue = inputData[inputIndex] -
- boost::numeric_cast<AccumulatorType>(inputOffset);
-
- }
- sum += filterValue * inputValue;
- }
- }
- }
-
- if (data.m_Parameters.m_BiasEnabled)
- {
- sum += biasData[cOutput];
- }
-
- if (outputScale != 0.0f)
- {
- float multiplier = (inputScale * filterScale) / outputScale;
- // Apply the multiplier to sum, but do so using some quantized arithmetic which is consistent
- // with the AndroidNN CPU implementation. This should be (roughly) equivalent to:
- // sum = std::round(multiplier * sum + outputOffset);
- sum = boost::numeric_cast<AccumulatorType>(
- QuantizedMultiplierSmallerThanOne(multiplier) * boost::numeric_cast<int32_t>(sum))
- + boost::numeric_cast<AccumulatorType>(outputOffset);
- sum = std::min<AccumulatorType>(std::max<AccumulatorType>(sum, 0), 255);
- }
-
- output.Get(batchIdx, cOutput, yOutput, xOutput) = boost::numeric_cast<InputType>(sum);
- }
- }
- }
- }
-}
-
void Convolve(const TensorShape& rInputShape,
Decoder<float>& rInputDecoder,
const TensorShape& rOutputShape,
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-23 08:22:42 +0000