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Diffstat (limited to 'src/backends/backendsCommon/WorkloadData.hpp')
| -rw-r--r-- | src/backends/backendsCommon/WorkloadData.hpp | 14 |
1 files changed, 13 insertions, 1 deletions
diff --git a/src/backends/backendsCommon/WorkloadData.hpp b/src/backends/backendsCommon/WorkloadData.hpp index 77d4209657..11ce2cb44f 100644 --- a/src/backends/backendsCommon/WorkloadData.hpp +++ b/src/backends/backendsCommon/WorkloadData.hpp @@ -208,7 +208,19 @@ struct Convolution2dQueueDescriptor : QueueDescriptorWithParameters<Convolution2 void Validate(const WorkloadInfo& workloadInfo) const; }; -// Depthwise Convolution 2D layer workload data. +/// Depthwise Convolution 2D layer workload data. +/// +/// @note +/// The weights are in the format [1, H, W, I*M]. Where I is the input channel size, M the depthwise mutliplier and +/// H, W is the height and width of the filter kernel. If per channel quantization is applied +/// the weights will be quantized along the last dimension/axis (I*M) which corresponds to the output channel size. +/// If per channel quantization is applied the weights tensor will have I*M scales, one for each dimension +/// of the quantization axis. You have to be aware of this when reshaping the weights tensor. +/// Splitting the I*M axis, e.g. [1, H, W, I*M] --> [H, W, I, M], won't work without taking care of the +/// corresponding quantization scales. +/// If there is no per channel quantization applied reshaping the weights tensor won't cause any issues. There are +/// preconfigured permutation functions available @link WorkloadUtils.hpp here. +/// struct DepthwiseConvolution2dQueueDescriptor : QueueDescriptorWithParameters<DepthwiseConvolution2dDescriptor> { DepthwiseConvolution2dQueueDescriptor() |