MPSCNNBinaryFullyConnected(3)
NAME
MPSCNNBinaryFullyConnected
SYNOPSIS
#import <MPSCNNConvolution.h>
Inherits MPSCNNBinaryConvolution.
Instance Methods
(nonnull instancetype) -
initWithDevice:convolutionData:scaleValue:type:flags:
(nonnull instancetype) -
initWithDevice:convolutionData:outputBiasTerms:outputScaleTerms:inputBiasTerms:inputScaleTerms:type:flags:
(nullable instancetype) - initWithCoder:device:
(nonnull instancetype) - initWithDevice:
Additional Inherited Members
Detailed Description
This depends on Metal.framework The MPSCNNBinaryFullyConnected
specifies a fully connected convolution layer with binary weights and
optionally binarized input image. See MPSCNNFullyConnected for details
on the fully connected layer and MPSCNNBinaryConvolution for binary
convolutions.
The default padding policy for MPSCNNBinaryConvolution is different
from most filters. It uses MPSNNPaddingMethodSizeValidOnly instead of
MPSNNPaddingMethodSizeSame.
Method Documentation
- (nullable instancetype) initWithCoder: (NSCoder *__nonnull)
aDecoder(nonnull id< MTLDevice >) device
NSSecureCoding compatability While the standard
NSSecureCoding/NSCoding method -initWithCoder: should work, since the
file can't know which device your data is allocated on, we have to
guess and may guess incorrectly. To avoid that problem, use
initWithCoder:device instead.
Parameters:
aDecoder The NSCoder subclass with your serialized MPSKernel
device The MTLDevice on which to make the MPSKernel
Returns:
A new MPSKernel object, or nil if failure.
Reimplemented from MPSCNNBinaryConvolution.
- (nonnull instancetype) initWithDevice: (nonnull id< MTLDevice >) device
Standard init with default properties per filter type
Parameters:
device The device that the filter will be used on. May not be NULL.
Returns:
A pointer to the newly initialized object. This will fail,
returning nil if the device is not supported. Devices must be
MTLFeatureSet_iOS_GPUFamily2_v1 or later.
Reimplemented from MPSCNNBinaryConvolution.
- (nonnull instancetype) initWithDevice: (nonnull id< MTLDevice >)
device(nonnull id< MPSCNNConvolutionDataSource >) convolutionData(const
float *__nullable) outputBiasTerms(const float *__nullable)
outputScaleTerms(const float *__nullable) inputBiasTerms(const float
*__nullable) inputScaleTerms(MPSCNNBinaryConvolutionType)
type(MPSCNNBinaryConvolutionFlags) flags
Initializes a binary fully connected kernel with binary weights as well
as both pre and post scaling terms.
Parameters:
device The MTLDevice on which this MPSCNNBinaryFullyConnected
filter will be used
convolutionData A pointer to a object that conforms to the
MPSCNNConvolutionDataSource protocol. The
MPSCNNConvolutionDataSource protocol declares the methods that an
instance of MPSCNNBinaryFullyConnected uses to obtain the weights
and the convolution descriptor. Each entry in the
convolutionData:weights array is a 32-bit unsigned integer value
and each bit represents one filter weight (given in machine byte
order). The featurechannel indices increase from the least
significant bit within the 32-bits. The number of entries is =
ceil( inputFeatureChannels/32.0 ) * outputFeatureChannels *
kernelHeight * kernelWidth The layout of filter weight is so that
it can be reinterpreted as a 4D tensor (array) weight[
outputChannels ][ kernelHeight ][ kernelWidth ][ ceil(
inputChannels / 32.0 ) ] (The ordering of the reduction from 4D
tensor to 1D is per C convention. The index based on inputchannels
varies most rapidly, followed by kernelWidth, then kernelHeight and
finally outputChannels varies least rapidly.)
outputBiasTerms A pointer to bias terms to be applied to the
convolution output. Each entry is a float value. The number of
entries is = numberOfOutputFeatureMaps. If nil then 0.0 is used for
bias. The values stored in the pointer are copied in and the array
can be freed after this function returns.
outputScaleTerms A pointer to scale terms to be applied to binary
convolution results per output feature channel. Each entry is a
float value. The number of entries is = numberOfOutputFeatureMaps.
If nil then 1.0 is used. The values stored in the pointer are
copied in and the array can be freed after this function returns.
inputBiasTerms A pointer to offset terms to be applied to the input
before convolution and before input scaling. Each entry is a float
value. The number of entries is 'inputFeatureChannels'. If NULL
then 0.0 is used for bias. The values stored in the pointer are
copied in and the array can be freed after this function returns.
inputScaleTerms A pointer to scale terms to be applied to the input
before convolution, but after input biasing. Each entry is a float
value. The number of entries is 'inputFeatureChannels'. If nil then
1.0 is used. The values stored in the pointer are copied in and the
array can be freed after this function returns.
type What kind of binarization strategy is to be used.
flags See documentation above and documentation of
MPSCNNBinaryConvolutionFlags.
Returns:
A valid MPSCNNBinaryFullyConnected object or nil, if failure.
Reimplemented from MPSCNNBinaryConvolution.
- (nonnull instancetype) initWithDevice: (nonnull id< MTLDevice >)
device(nonnull id< MPSCNNConvolutionDataSource >)
convolutionData(float) scaleValue(MPSCNNBinaryConvolutionType)
type(MPSCNNBinaryConvolutionFlags) flags
Initializes a binary fully connected kernel with binary weights and a
single scaling term.
Parameters:
device The MTLDevice on which this MPSCNNBinaryFullyConnected
filter will be used
convolutionData A pointer to a object that conforms to the
MPSCNNConvolutionDataSource protocol. The
MPSCNNConvolutionDataSource protocol declares the methods that an
instance of MPSCNNBinaryFullyConnected uses to obtain the weights
and bias terms as well as the convolution descriptor. Each entry in
the convolutionData:weights array is a 32-bit unsigned integer
value and each bit represents one filter weight (given in machine
byte order). The featurechannel indices increase from the least
significant bit within the 32-bits. The number of entries is =
ceil( inputFeatureChannels/32.0 ) * outputFeatureChannels *
kernelHeight * kernelWidth The layout of filter weight is so that
it can be reinterpreted as a 4D tensor (array) weight[
outputChannels ][ kernelHeight ][ kernelWidth ][ ceil(
inputChannels / 32.0 ) ] (The ordering of the reduction from 4D
tensor to 1D is per C convention. The index based on inputchannels
varies most rapidly, followed by kernelWidth, then kernelHeight and
finally outputChannels varies least rapidly.)
scaleValue A single floating point value used to scale the entire
convolution. Each entry is a float value. The number of entries is
'inputFeatureChannels'. If nil then 1.0 is used.
type What kind of binarization strategy is to be used.
flags See documentation above and documentation of
MPSCNNBinaryConvolutionFlags.
Returns:
A valid MPSCNNBinaryFullyConnected object or nil, if failure.
Reimplemented from MPSCNNBinaryConvolution.
Author
Generated automatically by Doxygen for
MetalPerformanceShaders.framework from the source code.
Version MetalPerformanceShaders-Thu2Jul 13 2017 MPSCNNBinaryFullyConnected(3)
Mac OS X 10.12.6 - Generated Sun Oct 29 14:51:45 CDT 2017
