diff options
Diffstat (limited to 'src/backends/reference/workloads/ConvImpl.cpp')
| -rw-r--r-- | src/backends/reference/workloads/ConvImpl.cpp | 88 |
1 files changed, 61 insertions, 27 deletions
diff --git a/src/backends/reference/workloads/ConvImpl.cpp b/src/backends/reference/workloads/ConvImpl.cpp index 7e8b8fffb6..f11c351c61 100644 --- a/src/backends/reference/workloads/ConvImpl.cpp +++ b/src/backends/reference/workloads/ConvImpl.cpp @@ -95,19 +95,45 @@ void Convolve(const TensorShape& rInputShape, const unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex(); const unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex(); - unsigned int depthMultiplier = depthwise ? rFilterShape[0] : 1; - unsigned int inputChannels = depthwise ? rFilterShape[1] : rFilterShape[channelsIndex]; - unsigned int outputChannels = depthwise ? inputChannels * depthMultiplier : rFilterShape[0]; + const unsigned int depthMultiplier = depthwise ? rFilterShape[0] : 1; + const unsigned int inputChannels = depthwise ? rFilterShape[1] : rFilterShape[channelsIndex]; + const unsigned int outputChannels = depthwise ? inputChannels * depthMultiplier : rFilterShape[0]; - unsigned int batchSize = rOutputShape[0]; - unsigned int outputHeight = rOutputShape[heightIndex]; - unsigned int outputWidth = rOutputShape[widthIndex]; - unsigned int inputHeight = rInputShape[heightIndex]; - unsigned int inputWidth = rInputShape[widthIndex]; + const unsigned int batchSize = rOutputShape[0]; + const unsigned int outputHeight = rOutputShape[heightIndex]; + const unsigned int outputWidth = rOutputShape[widthIndex]; + const unsigned int inputHeight = rInputShape[heightIndex]; + const unsigned int inputWidth = rInputShape[widthIndex]; - unsigned int filterHeight = depthwise ? rFilterShape[2] : rFilterShape[heightIndex]; - unsigned int filterWidth = depthwise ? rFilterShape[3] : rFilterShape[widthIndex]; + const unsigned int filterHeight = depthwise ? rFilterShape[2] : rFilterShape[heightIndex]; + const unsigned int filterWidth = depthwise ? rFilterShape[3] : rFilterShape[widthIndex]; + const std::vector<float> inputVec = rInputDecoder.DecodeTensor(rInputShape.GetNumElements()); + + uint32_t channelStepSize; + if (depthwise) + { + channelStepSize = filterHeight * filterWidth; + } + else + { + if (dataLayoutIndexed.GetDataLayout() == DataLayout::NHWC) + { + channelStepSize = rFilterShape[3]; + } + else + { + channelStepSize = rFilterShape[1] * rFilterShape[2] * rFilterShape[3]; + } + } + + const std::vector<float> filterVec = rFilterDecoder.DecodeTensor(rFilterShape.GetNumElements(), + channelStepSize, + depthMultiplier); + const std::vector<float> biasVec = biasEnabled ? + pBiasDecoder->DecodeTensor(outputChannels) : std::vector<float>(); + + unsigned int depthwiseMultiplierIdx = 0; for (unsigned int batchIdx = 0; batchIdx < batchSize; batchIdx++) { for (unsigned int cOutput = 0; cOutput < outputChannels; cOutput++) @@ -117,15 +143,15 @@ void Convolve(const TensorShape& rInputShape, for (unsigned int xOutput = 0; xOutput < outputWidth; xOutput++) { // This loop goes over each output element. - float sum = 0.0f; + float sum = 0.0f; // 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) { + depthwiseMultiplierIdx = 0; cInput = cOutput / depthMultiplier; depthwiseMultiplierIdx = cOutput % depthMultiplier; } @@ -149,7 +175,7 @@ void Convolve(const TensorShape& rInputShape, { // Keep this implementation, as using DataLayoutIndexed::GetIndex causes great // performance regression. - if (dataLayout == DataLayout::NHWC) + if (dataLayoutIndexed.GetDataLayout() == DataLayout::NHWC) { filterIndex = cOutput * filterHeight * filterWidth * inputChannels + yFilter * filterWidth * inputChannels + @@ -159,15 +185,12 @@ void Convolve(const TensorShape& rInputShape, else { filterIndex = cOutput * filterWidth * filterHeight * inputChannels + - cInput * filterWidth * filterHeight + + cInput * filterWidth * filterHeight + yFilter * filterWidth + xFilter; } } - rFilterDecoder.SetIndex(filterIndex, cOutput); - float filterValue = rFilterDecoder.Get(); - unsigned int yInput = yOutput * yStride + yFilter * yDilation; unsigned int xInput = xOutput * xStride + xFilter * xDilation; @@ -175,7 +198,7 @@ void Convolve(const TensorShape& rInputShape, // Check if we're in the padding. if (yInput < paddingTop || yInput >= inputHeight + paddingTop || - xInput < paddingLeft || xInput >= inputWidth + paddingLeft ) + xInput < paddingLeft || xInput >= inputWidth + paddingLeft) { inputValue = 0.0f; } @@ -185,9 +208,9 @@ void Convolve(const TensorShape& rInputShape, // Keep this implementation, as using DataLayoutIndexed::GetIndex causes great // performance regression. - if (dataLayout == DataLayout::NHWC) + if (dataLayoutIndexed.GetDataLayout() == DataLayout::NHWC) { - inputIndex = batchIdx * inputHeight * inputWidth * inputChannels + + inputIndex = batchIdx * inputHeight * inputWidth * inputChannels + (yInput - paddingTop) * inputWidth * inputChannels + (xInput - paddingLeft) * inputChannels + cInput; @@ -199,23 +222,34 @@ void Convolve(const TensorShape& rInputShape, inputWidth * (yInput - paddingTop) + xInput - paddingLeft; } - - rInputDecoder[inputIndex]; - inputValue = rInputDecoder.Get(); + inputValue = inputVec[inputIndex]; } - sum += filterValue * inputValue; + sum += filterVec[filterIndex] * inputValue; } } } if (biasEnabled) { - (*pBiasDecoder).SetIndex(cOutput, cOutput); - sum += pBiasDecoder->Get(); + sum += biasVec[cOutput]; } - unsigned int outIdx = dataLayoutIndexed.GetIndex(rOutputShape, batchIdx, cOutput, yOutput, xOutput); + unsigned int outIdx; + if (dataLayoutIndexed.GetDataLayout() == DataLayout::NHWC) + { + outIdx = batchIdx * outputHeight * outputWidth * outputChannels + + yOutput * outputWidth * outputChannels + + xOutput * outputChannels + + cOutput; + } + else + { + outIdx = batchIdx * outputHeight * outputWidth * outputChannels + + cOutput * outputHeight * outputWidth + + yOutput * outputWidth + + xOutput; + } rOutputEncoder[outIdx]; rOutputEncoder.Set(sum); |