MPSNNConcatenationNode(3) MetalPerformanceShaders.framework MPSNNConcatenationNode(3)
NAME
MPSNNConcatenationNode
SYNOPSIS
#import <MPSNNGraphNodes.h>
Inherits MPSNNFilterNode.
Instance Methods
(nonnull instancetype) - initWithSources:
(MPSNNGradientFilterNode *__nonnull) - gradientFilterWithSources:
Class Methods
(nonnull instancetype) + nodeWithSources:
Additional Inherited Members
Detailed Description
Node representing a the concatenation (in the feature channel dimension) of the results from one or more kernels
Method Documentation
- (MPSNNGradientFilterNode*__nonnull) gradientFilterWithSources: (NSArray< MPSNNImageNode * > *__nonnull) gradientImages
Concatenation returns multiple gradient filters. Use -gradientFiltersWithSources: instead.
Reimplemented from MPSNNFilterNode.
- (nonnull instancetype) initWithSources: (NSArray< MPSNNImageNode * > *__nonnull) sourceNodes
Init a node that concatenates feature channels from multiple images In some neural network designs, it is necessary to append feature
channels from one neural network filter to the results of another. If we have three image nodes with M, N and O feature channels in them,
passed to -initWithSources: as @[imageM, imageN, imageO], then feature channels [0,M-1] will be drawn from image M, feature channels [M,
M+N-1] will be drawn from image N and feature channels [M+N, M+N+O-1] will be drawn from image O.
As all images are padded out to a multiple of four feature channels, M, N and O here are also multiples of four, even when the MPSImages
are not. That is, if the image is 23 feature channels and one channel of padding, it takes up 24 feature channels worth of space in the
concatenated result.
Performance Note: Generally, concatenation is free as long as all of the sourceNodes are produced by filters in the same MPSNNGraph. Most
MPSCNNKernels have the ability to write their results at a feature channel offset within a target MPSImage. However, if the MPSNNImageNode
source nodes come from images external to the MPSNNGraph, then we have to do a copy operation to assemble the concatenated node. As a
result, when deciding where to break a large logical graph into multiple smaller MPSNNGraphs, it is better for concatenations to appear at
the ends of subgraphs when possible rather than at the start, to the extent that all the images used in the concatenation are produced by
that subgraph.
Parameters:
sourceNodes The MPSNNImageNode representing the source MPSImages for the filter
Returns:
A new MPSNNFilter node that concatenates its inputs.
+ (nonnull instancetype) nodeWithSources: (NSArray< MPSNNImageNode * > *__nonnull) sourceNodes
Init a autoreleased node that concatenates feature channels from multiple images In some neural network designs, it is necessary to append
feature channels from one neural network filter to the results of another. If we have three image nodes with M, N and O feature channels in
them, passed to -initWithSources: as @[imageM, imageN, imageO], then feature channels [0,M-1] will be drawn from image M, feature channels
[M, M+N-1] will be drawn from image N and feature channels [M+N, M+N+O-1] will be drawn from image O.
As all images are padded out to a multiple of four feature channels, M, N and O here are also multiples of four, even when the MPSImages
are not. That is, if the image is 23 feature channels and one channel of padding, it takes up 24 feature channels worth of space in the
concatenated result.
Performance Note: Generally, concatenation is free as long as all of the sourceNodes are produced by filters in the same MPSNNGraph. Most
MPSCNNKernels have the ability to write their results at a feature channel offset within a target MPSImage. However, if the MPSNNImageNode
source nodes come from images external to the MPSNNGraph, then we have to do a copy operation to assemble the concatenated node. As a
result, when deciding where to break a large logical graph into multiple smaller MPSNNGraphs, it is better for concatenations to appear at
the ends of subgraphs when possible rather than at the start, to the extent that all the images used in the concatenation are produced by
that subgraph.
Parameters:
sourceNodes The MPSNNImageNode representing the source MPSImages for the filter
Returns:
A new MPSNNFilter node that concatenates its inputs.
Author
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Version MetalPerformanceShaders-100 Thu Feb 8 2018 MPSNNConcatenationNode(3)