Package apple.metalperformanceshaders

Class MPSCNNConvolutionDescriptor

    • Constructor Detail

      • MPSCNNConvolutionDescriptor

        protected MPSCNNConvolutionDescriptor​(org.moe.natj.general.Pointer peer)

    • Method Detail

      • accessInstanceVariablesDirectly

        public static boolean accessInstanceVariablesDirectly()

      • allocWithZone

        public static java.lang.Object allocWithZone​(org.moe.natj.general.ptr.VoidPtr zone)

      • automaticallyNotifiesObserversForKey

        public static boolean automaticallyNotifiesObserversForKey​(java.lang.String key)

      • cancelPreviousPerformRequestsWithTarget

        public static void cancelPreviousPerformRequestsWithTarget​(java.lang.Object aTarget)

      • cancelPreviousPerformRequestsWithTargetSelectorObject

        public static void cancelPreviousPerformRequestsWithTargetSelectorObject​(java.lang.Object aTarget,
                                                                         org.moe.natj.objc.SEL aSelector,
                                                                         java.lang.Object anArgument)

      • classFallbacksForKeyedArchiver

        public static NSArray<java.lang.String> classFallbacksForKeyedArchiver()

      • classForKeyedUnarchiver

        public static org.moe.natj.objc.Class classForKeyedUnarchiver()

      • cnnConvolutionDescriptorWithKernelWidthKernelHeightInputFeatureChannelsOutputFeatureChannelsNeuronFilter

        public static java.lang.Object cnnConvolutionDescriptorWithKernelWidthKernelHeightInputFeatureChannelsOutputFeatureChannelsNeuronFilter​(long kernelWidth,
                                                                                                                                        long kernelHeight,
                                                                                                                                        long inputFeatureChannels,
                                                                                                                                        long outputFeatureChannels,
                                                                                                                                        MPSCNNNeuron neuronFilter)
        This method is deprecated. Please use neuronType, neuronParameterA and neuronParameterB properites to fuse neuron with convolution.
        Parameters:
        kernelWidth - The width of the filter window. Must be > 0. Large values will take a long time.
        kernelHeight - The height of the filter window. Must be > 0. Large values will take a long time.
        inputFeatureChannels - The number of feature channels in the input image. Must be >= 1.
        outputFeatureChannels - The number of feature channels in the output image. Must be >= 1.
        neuronFilter - An optional neuron filter that can be applied to the output of convolution.
        Returns:
        A valid MPSCNNConvolutionDescriptor object or nil, if failure.

      • debugDescription_static

        public static java.lang.String debugDescription_static()

      • description_static

        public static java.lang.String description_static()

      • hash_static

        public static long hash_static()

      • instanceMethodSignatureForSelector

        public static NSMethodSignature instanceMethodSignatureForSelector​(org.moe.natj.objc.SEL aSelector)

      • instancesRespondToSelector

        public static boolean instancesRespondToSelector​(org.moe.natj.objc.SEL aSelector)

      • isSubclassOfClass

        public static boolean isSubclassOfClass​(org.moe.natj.objc.Class aClass)

      • keyPathsForValuesAffectingValueForKey

        public static NSSet<java.lang.String> keyPathsForValuesAffectingValueForKey​(java.lang.String key)

      • new_objc

        public static java.lang.Object new_objc()

      • resolveClassMethod

        public static boolean resolveClassMethod​(org.moe.natj.objc.SEL sel)

      • resolveInstanceMethod

        public static boolean resolveInstanceMethod​(org.moe.natj.objc.SEL sel)

      • setVersion_static

        public static void setVersion_static​(long aVersion)

      • superclass_static

        public static org.moe.natj.objc.Class superclass_static()

      • version_static

        public static long version_static()

      • copyWithZone

        public java.lang.Object copyWithZone​(org.moe.natj.general.ptr.VoidPtr zone)
        Specified by:
        copyWithZone in interface NSCopying

      • groups

        public long groups()
        [@property] groups Number of groups input and output channels are divided into. The default value is 1. Groups lets you reduce the parameterization. If groups is set to n, input is divided into n groups with inputFeatureChannels/n channels in each group. Similarly output is divided into n groups with outputFeatureChannels/n channels in each group. ith group in input is only connected to ith group in output so number of weights (parameters) needed is reduced by factor of n. Both inputFeatureChannels and outputFeatureChannels must be divisible by n and number of channels in each group must be multiple of 4.

      • inputFeatureChannels

        public long inputFeatureChannels()
        [@property] inputFeatureChannels The number of feature channels per pixel in the input image.

      • kernelHeight

        public long kernelHeight()
        [@property] kernelHeight The height of the filter window. The default value is 3. Any positive non-zero value is valid, including even values. The position of the top edge of the filter window is given by offset.y - (kernelHeight>>1)

      • kernelWidth

        public long kernelWidth()
        [@property] kernelWidth The width of the filter window. The default value is 3. Any positive non-zero value is valid, including even values. The position of the left edge of the filter window is given by offset.x - (kernelWidth>>1)

      • neuron

        public MPSCNNNeuron neuron()
        [@property] neuron MPSCNNNeuron filter to be applied as part of convolution. This is applied after BatchNormalization in the end. Default is nil. This is deprecated. You dont need to create MPSCNNNeuron object to fuse with convolution. Use neuron properties in this descriptor.

      • outputFeatureChannels

        public long outputFeatureChannels()
        [@property] outputFeatureChannels The number of feature channels per pixel in the output image.

      • setGroups

        public void setGroups​(long value)
        [@property] groups Number of groups input and output channels are divided into. The default value is 1. Groups lets you reduce the parameterization. If groups is set to n, input is divided into n groups with inputFeatureChannels/n channels in each group. Similarly output is divided into n groups with outputFeatureChannels/n channels in each group. ith group in input is only connected to ith group in output so number of weights (parameters) needed is reduced by factor of n. Both inputFeatureChannels and outputFeatureChannels must be divisible by n and number of channels in each group must be multiple of 4.

      • setInputFeatureChannels

        public void setInputFeatureChannels​(long value)
        [@property] inputFeatureChannels The number of feature channels per pixel in the input image.

      • setKernelHeight

        public void setKernelHeight​(long value)
        [@property] kernelHeight The height of the filter window. The default value is 3. Any positive non-zero value is valid, including even values. The position of the top edge of the filter window is given by offset.y - (kernelHeight>>1)

      • setKernelWidth

        public void setKernelWidth​(long value)
        [@property] kernelWidth The width of the filter window. The default value is 3. Any positive non-zero value is valid, including even values. The position of the left edge of the filter window is given by offset.x - (kernelWidth>>1)

      • setNeuron

        public void setNeuron​(MPSCNNNeuron value)
        [@property] neuron MPSCNNNeuron filter to be applied as part of convolution. This is applied after BatchNormalization in the end. Default is nil. This is deprecated. You dont need to create MPSCNNNeuron object to fuse with convolution. Use neuron properties in this descriptor.

      • setOutputFeatureChannels

        public void setOutputFeatureChannels​(long value)
        [@property] outputFeatureChannels The number of feature channels per pixel in the output image.

      • setStrideInPixelsX

        public void setStrideInPixelsX​(long value)
        [@property] strideInPixelsX The output stride (downsampling factor) in the x dimension. The default value is 1.

      • setStrideInPixelsY

        public void setStrideInPixelsY​(long value)
        [@property] strideInPixelsY The output stride (downsampling factor) in the y dimension. The default value is 1.

      • strideInPixelsX

        public long strideInPixelsX()
        [@property] strideInPixelsX The output stride (downsampling factor) in the x dimension. The default value is 1.

      • strideInPixelsY

        public long strideInPixelsY()
        [@property] strideInPixelsY The output stride (downsampling factor) in the y dimension. The default value is 1.

      • cnnConvolutionDescriptorWithKernelWidthKernelHeightInputFeatureChannelsOutputFeatureChannels

        public static java.lang.Object cnnConvolutionDescriptorWithKernelWidthKernelHeightInputFeatureChannelsOutputFeatureChannels​(long kernelWidth,
                                                                                                                            long kernelHeight,
                                                                                                                            long inputFeatureChannels,
                                                                                                                            long outputFeatureChannels)
        Creates a convolution descriptor.
        Parameters:
        kernelWidth - The width of the filter window. Must be > 0. Large values will take a long time.
        kernelHeight - The height of the filter window. Must be > 0. Large values will take a long time.
        inputFeatureChannels - The number of feature channels in the input image. Must be >= 1.
        outputFeatureChannels - The number of feature channels in the output image. Must be >= 1.
        Returns:
        A valid MPSCNNConvolutionDescriptor object or nil, if failure.

      • dilationRateX

        public long dilationRateX()
        [@property] dilationRateX dilationRateX property can be used to implement dilated convolution as described in https://arxiv.org/pdf/1511.07122v3.pdf to aggregate global information in dense prediction problems. Default value is 1. When set to value > 1, original kernel width, kW is dilated to kW_Dilated = (kW-1)*dilationRateX + 1 by inserting d-1 zeros between consecutive entries in each row of the original kernel. The kernel is centered based on kW_Dilated.

      • dilationRateY

        public long dilationRateY()
        [@property] dilationRateY dilationRateY property can be used to implement dilated convolution as described in https://arxiv.org/pdf/1511.07122v3.pdf to aggregate global information in dense prediction problems. Default value is 1. When set to value > 1, original kernel height, kH is dilated to kH_Dilated = (kH-1)*dilationRateY + 1 by inserting d-1 rows of zeros between consecutive row of the original kernel. The kernel is centered based on kH_Dilated.

      • neuronParameterA

        public float neuronParameterA()
        Getter funtion for neuronType set using setNeuronType:parameterA:parameterB method

      • neuronParameterB

        public float neuronParameterB()
        Getter funtion for neuronType set using setNeuronType:parameterA:parameterB method

      • neuronType

        public int neuronType()
        Getter funtion for neuronType set using setNeuronType:parameterA:parameterB method

      • setBatchNormalizationParametersForInferenceWithMeanVarianceGammaBetaEpsilon

        public void setBatchNormalizationParametersForInferenceWithMeanVarianceGammaBetaEpsilon​(org.moe.natj.general.ptr.ConstFloatPtr mean,
                                                                                        org.moe.natj.general.ptr.ConstFloatPtr variance,
                                                                                        org.moe.natj.general.ptr.ConstFloatPtr gamma,
                                                                                        org.moe.natj.general.ptr.ConstFloatPtr beta,
                                                                                        float epsilon)
        Adds batch normalization for inference, it copies all the float arrays provided, expecting outputFeatureChannels elements in each. This method will be used to pass in batch normalization parameters to the convolution during the init call. For inference we modify weights and bias going in convolution or Fully Connected layer to combine and optimize the layers. w: weights for a corresponding output feature channel b: bias for a corresponding output feature channel W: batch normalized weights for a corresponding output feature channel B: batch normalized bias for a corresponding output feature channel I = gamma / sqrt(variance + epsilon), J = beta - ( I * mean ) W = w * I B = b * I + J Every convolution has (OutputFeatureChannel * kernelWidth * kernelHeight * InputFeatureChannel) weights I, J are calculated, for every output feature channel separately to get the corresponding weights and bias Thus, I, J are calculated and then used for every (kernelWidth * kernelHeight * InputFeatureChannel) weights, and this is done OutputFeatureChannel number of times for each output channel. thus, internally, batch normalized weights are computed as: W[no][i][j][ni] = w[no][i][j][ni] * I[no] no: index into outputFeatureChannel i : index into kernel Height j : index into kernel Width ni: index into inputFeatureChannel One usually doesn't see a bias term and batch normalization together as batch normalization potentially cancels out the bias term after training, but in MPS if the user provides it, batch normalization will use the above formula to incorporate it, if user does not have bias terms then put a float array of zeroes in the convolution init for bias terms of each output feature channel. this comes from: https://arxiv.org/pdf/1502.03167v3.pdf Note: in certain cases the batch normalization parameters will be cached by the MPSNNGraph or the MPSCNNConvolution. If the batch normalization parameters change after either is made, behavior is undefined.
        Parameters:
        mean - Pointer to an array of floats of mean for each output feature channel
        variance - Pointer to an array of floats of variance for each output feature channel
        gamma - Pointer to an array of floats of gamma for each output feature channel
        beta - Pointer to an array of floats of beta for each output feature channel
        epsilon - A small float value used to have numerical stability in the code

      • setDilationRateX

        public void setDilationRateX​(long value)
        [@property] dilationRateX dilationRateX property can be used to implement dilated convolution as described in https://arxiv.org/pdf/1511.07122v3.pdf to aggregate global information in dense prediction problems. Default value is 1. When set to value > 1, original kernel width, kW is dilated to kW_Dilated = (kW-1)*dilationRateX + 1 by inserting d-1 zeros between consecutive entries in each row of the original kernel. The kernel is centered based on kW_Dilated.

      • setDilationRateY

        public void setDilationRateY​(long value)
        [@property] dilationRateY dilationRateY property can be used to implement dilated convolution as described in https://arxiv.org/pdf/1511.07122v3.pdf to aggregate global information in dense prediction problems. Default value is 1. When set to value > 1, original kernel height, kH is dilated to kH_Dilated = (kH-1)*dilationRateY + 1 by inserting d-1 rows of zeros between consecutive row of the original kernel. The kernel is centered based on kH_Dilated.

      • setNeuronToPReLUWithParametersA

        public void setNeuronToPReLUWithParametersA​(NSData A)
        Add per-channel neuron parameters A for PReLu neuron activation functions. This method sets the neuron to PReLU, zeros parameters A and B and sets the per-channel neuron parameters A to an array containing a unique value of A for each output feature channel. If the neuron function is f(v,a,b), it will apply OutputImage(x,y,i) = f( ConvolutionResult(x,y,i), A[i], B[i] ) where i in [0,outputFeatureChannels-1] See https://arxiv.org/pdf/1502.01852.pdf for details. All other neuron types, where parameter A and parameter B are shared across channels must be set using -setNeuronOfType:parameterA:parameterB: If batch normalization parameters are set, batch normalization will occur before neuron application i.e. output of convolution is first batch normalized followed by neuron activation. This function automatically sets neuronType to MPSCNNNeuronTypePReLU. Note: in certain cases the neuron descriptor will be cached by the MPSNNGraph or the MPSCNNConvolution. If the neuron type changes after either is made, behavior is undefined.
        Parameters:
        A - An array containing per-channel float values for neuron parameter A. Number of entries must be equal to outputFeatureChannels.

      • setNeuronTypeParameterAParameterB

        public void setNeuronTypeParameterAParameterB​(int neuronType,
                                              float parameterA,
                                              float parameterB)
        Adds a neuron activation function to convolution descriptor. This mathod can be used to add a neuron activation funtion of given type with associated scalar parameters A and B that are shared across all output channels. Neuron activation fucntion is applied to output of convolution. This is a per-pixel operation that is fused with convolution kernel itself for best performance. Note that this method can only be used to fuse neuron of kind for which parameters A and B are shared across all channels of convoution output. It is an error to call this method for neuron activation functions like MPSCNNNeuronTypePReLU, which require per-channel parameter values. For those kind of neuron activation functions, use appropriate setter functions. Note: in certain cases, the neuron descriptor will be cached by the MPSNNGraph or the MPSCNNConvolution. If the neuron type changes after either is made, behavior is undefined.
        Parameters:
        neuronType - type of neuron activation function. For full list see MPSCNNNeuronType.h
        parameterA - parameterA of neuron activation that is shared across all channels of convolution output.
        parameterB - parameterB of neuron activation that is shared across all channels of convolution output.

      • supportsSecureCoding

        public static boolean supportsSecureCoding()

      • _supportsSecureCoding

        public boolean _supportsSecureCoding()
        Description copied from interface: NSSecureCoding
        This property must return YES on all classes that allow secure coding. Subclasses of classes that adopt NSSecureCoding and override initWithCoder: must also override this method and return YES. The Secure Coding Guide should be consulted when writing methods that decode data.
        Specified by:
        _supportsSecureCoding in interface NSSecureCoding

      • fusedNeuronDescriptor

        public MPSNNNeuronDescriptor fusedNeuronDescriptor()
        [@property] fusedNeuronDescriptor This mathod can be used to add a neuron activation funtion of given type with associated scalar parameters A and B that are shared across all output channels. Neuron activation fucntion is applied to output of convolution. This is a per-pixel operation that is fused with convolution kernel itself for best performance. Note that this method can only be used to fuse neuron of kind for which parameters A and B are shared across all channels of convoution output. It is an error to call this method for neuron activation functions like MPSCNNNeuronTypePReLU, which require per-channel parameter values. For those kind of neuron activation functions, use appropriate setter functions. Default is descriptor with neuronType MPSCNNNeuronTypeNone. Note: in certain cases the neuron descriptor will be cached by the MPSNNGraph or the MPSCNNConvolution. If the neuron type changes after either is made, behavior is undefined.

      • setFusedNeuronDescriptor

        public void setFusedNeuronDescriptor​(MPSNNNeuronDescriptor value)
        [@property] fusedNeuronDescriptor This mathod can be used to add a neuron activation funtion of given type with associated scalar parameters A and B that are shared across all output channels. Neuron activation fucntion is applied to output of convolution. This is a per-pixel operation that is fused with convolution kernel itself for best performance. Note that this method can only be used to fuse neuron of kind for which parameters A and B are shared across all channels of convoution output. It is an error to call this method for neuron activation functions like MPSCNNNeuronTypePReLU, which require per-channel parameter values. For those kind of neuron activation functions, use appropriate setter functions. Default is descriptor with neuronType MPSCNNNeuronTypeNone. Note: in certain cases the neuron descriptor will be cached by the MPSNNGraph or the MPSCNNConvolution. If the neuron type changes after either is made, behavior is undefined.