Struct MPSCNNSubPixelConvolutionDescriptor 

pub struct MPSCNNSubPixelConvolutionDescriptor { /* private fields */ }

Available on crate features MPSCNNConvolution and MPSNeuralNetwork only.

MPSCNNSubPixelConvolutionDescriptor can be used to create MPSCNNConvolution object that does sub pixel upsamling and reshaping opeartion as described in http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Shi_Real-Time_Single_Image_CVPR_2016_paper.pdf

Conceptually MPSCNNConvolution with subPixelScaleFactor > 1 can be thought of as filter performing regular CNN convolution producing N output feature channels at each pixel of an intermediate MPSImage followed by a kernel that rearranges/reshapes these N channels at each pixel of intermediate MPSImage into a pixel block of size subPixelScaleFactor x subPixelScaleFactor with N/(subPixelScaleFactor * subPixelScaleFactor) featureChannels at each pixel of this pixel block. Thus each pixel in intermedaite MPSImage with N channels map to subPixelScaleFactor x subPixelScaleFactor pixel block in final destination MPSImage with N/(subPixelScaleFactor * subPixelScaleFactor) featureChannels. MPSCNNConvolution with subPixelScaleFactor > 1 fuses the convolution and reshaping operation into single compute kernel thus not only saving DRAM roundtrip but also memory needed for intermediate MPSImage had these operation done separately. Let N be the value of outputFeatureChannels property and let r = subPixelScaleFactor. Conceptually Convolution will produce intermedaite image Io of dimensions (treated as 3D tensor) width x height x N where width = (clipRect.size.width + r - 1) / r height = (clipRect.size.height + r -1) / r Reshaping happens as follows

                   Destination[clipRect.origin.x+x][clipRect.origin.y+y][c] = Io[ floor(x/r) ][ floor(y/r) ][ (N/r^2) * ( r * mod(y,r) + mod(x,r) ) + c ]
                   where x in [0,clipRect.size.width-1], y in [0,clipRect.size.height-1], c in [0,N/r^2 - 1]

The following conditions must be met:

1. N (outputFeatureChannels) must be multiple of r^2 (subPixelScaleFactor * subPixelScaleFactor).
2. The destination MPSImage to encode call must have at least N/r^2 + destinationFeatureChannelOffset channels.
3. Number of feature channels in reshaped output image (N/r^2) can be any value when groups = 1 but must be multiple of 4 when groups > 1.

See also Apple’s documentation

Implementations

impl MPSCNNSubPixelConvolutionDescriptor

pub unsafe fn subPixelScaleFactor(&self) -> NSUInteger

Upsampling scale factor. Each pixel in input is upsampled into a subPixelScaleFactor x subPixelScaleFactor pixel block by rearranging the outputFeatureChannels as described above. Default value is 1.

pub unsafe fn setSubPixelScaleFactor(&self, sub_pixel_scale_factor: NSUInteger)

Setter for subPixelScaleFactor.

impl MPSCNNSubPixelConvolutionDescriptor

Methods declared on superclass MPSCNNConvolutionDescriptor.

pub unsafe fn initWithCoder( this: Allocated<Self>, a_decoder: &NSCoder, ) -> Option<Retained<Self>>

<NSSecureCoding

support

Safety

a_decoder possibly has further requirements.

pub unsafe fn cnnConvolutionDescriptorWithKernelWidth_kernelHeight_inputFeatureChannels_outputFeatureChannels_neuronFilter( kernel_width: NSUInteger, kernel_height: NSUInteger, input_feature_channels: NSUInteger, output_feature_channels: NSUInteger, neuron_filter: Option<&MPSCNNNeuron>, ) -> Retained<Self>

👎Deprecated

Available on crate features MPSCNNKernel and MPSCNNNeuron and MPSCore and MPSKernel only.

This method is deprecated. Please use neuronType, neuronParameterA and neuronParameterB properites to fuse neuron with convolution.

Parameter kernelWidth: The width of the filter window. Must be > 0. Large values will take a long time.

Parameter kernelHeight: The height of the filter window. Must be > 0. Large values will take a long time.

Parameter inputFeatureChannels: The number of feature channels in the input image. Must be >= 1.

Parameter outputFeatureChannels: The number of feature channels in the output image. Must be >= 1.

Parameter neuronFilter: An optional neuron filter that can be applied to the output of convolution.

Returns: A valid MPSCNNConvolutionDescriptor object or nil, if failure.

pub unsafe fn cnnConvolutionDescriptorWithKernelWidth_kernelHeight_inputFeatureChannels_outputFeatureChannels( kernel_width: NSUInteger, kernel_height: NSUInteger, input_feature_channels: NSUInteger, output_feature_channels: NSUInteger, ) -> Retained<Self>

Creates a convolution descriptor.

Parameter kernelWidth: The width of the filter window. Must be > 0. Large values will take a long time.

Parameter kernelHeight: The height of the filter window. Must be > 0. Large values will take a long time.

Parameter inputFeatureChannels: The number of feature channels in the input image. Must be >= 1.

Parameter outputFeatureChannels: The number of feature channels in the output image. Must be >= 1.

Returns: A valid MPSCNNConvolutionDescriptor object or nil, if failure.

impl MPSCNNSubPixelConvolutionDescriptor

Methods declared on superclass NSObject.

pub unsafe fn init(this: Allocated<Self>) -> Retained<Self>

pub unsafe fn new() -> Retained<Self>

Methods from Deref<Target = MPSCNNConvolutionDescriptor>

pub unsafe fn kernelWidth(&self) -> NSUInteger

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)

pub unsafe fn setKernelWidth(&self, kernel_width: NSUInteger)

Setter for kernelWidth.

pub unsafe fn kernelHeight(&self) -> NSUInteger

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)

pub unsafe fn setKernelHeight(&self, kernel_height: NSUInteger)

Setter for kernelHeight.

pub unsafe fn inputFeatureChannels(&self) -> NSUInteger

The number of feature channels per pixel in the input image.

pub unsafe fn setInputFeatureChannels(&self, input_feature_channels: NSUInteger)

Setter for inputFeatureChannels.

pub unsafe fn outputFeatureChannels(&self) -> NSUInteger

The number of feature channels per pixel in the output image.

pub unsafe fn setOutputFeatureChannels( &self, output_feature_channels: NSUInteger, )

Setter for outputFeatureChannels.

pub unsafe fn strideInPixelsX(&self) -> NSUInteger

The output stride (downsampling factor) in the x dimension. The default value is 1.

pub unsafe fn setStrideInPixelsX(&self, stride_in_pixels_x: NSUInteger)

Setter for strideInPixelsX.

pub unsafe fn strideInPixelsY(&self) -> NSUInteger

The output stride (downsampling factor) in the y dimension. The default value is 1.

pub unsafe fn setStrideInPixelsY(&self, stride_in_pixels_y: NSUInteger)

Setter for strideInPixelsY.

pub unsafe fn groups(&self) -> NSUInteger

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.

pub unsafe fn setGroups(&self, groups: NSUInteger)

Setter for groups.

pub unsafe fn dilationRateX(&self) -> NSUInteger

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.

pub unsafe fn setDilationRateX(&self, dilation_rate_x: NSUInteger)

Setter for dilationRateX.

pub unsafe fn dilationRateY(&self) -> NSUInteger

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.

pub unsafe fn setDilationRateY(&self, dilation_rate_y: NSUInteger)

Setter for dilationRateY.

pub unsafe fn fusedNeuronDescriptor(&self) -> Retained<MPSNNNeuronDescriptor>

Available on crate feature MPSCNNNeuron only.

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.

pub unsafe fn setFusedNeuronDescriptor( &self, fused_neuron_descriptor: &MPSNNNeuronDescriptor, )

Available on crate feature MPSCNNNeuron only.

Setter for fusedNeuronDescriptor.

pub unsafe fn neuron(&self) -> Option<Retained<MPSCNNNeuron>>

👎Deprecated

Available on crate features MPSCNNKernel and MPSCNNNeuron and MPSCore and MPSKernel only.

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.

pub unsafe fn setNeuron(&self, neuron: Option<&MPSCNNNeuron>)

👎Deprecated

Available on crate features MPSCNNKernel and MPSCNNNeuron and MPSCore and MPSKernel only.

Setter for neuron.

pub unsafe fn encodeWithCoder(&self, a_coder: &NSCoder)

<NSSecureCoding

support

Safety

a_coder possibly has further requirements.

pub unsafe fn setBatchNormalizationParametersForInferenceWithMean_variance_gamma_beta_epsilon( &self, mean: *const c_float, variance: *const c_float, gamma: *const c_float, beta: *const c_float, epsilon: c_float, )

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.

Parameter mean: Pointer to an array of floats of mean for each output feature channel

Parameter variance: Pointer to an array of floats of variance for each output feature channel

Parameter gamma: Pointer to an array of floats of gamma for each output feature channel

Parameter beta: Pointer to an array of floats of beta for each output feature channel

Parameter epsilon: A small float value used to have numerical stability in the code

Safety

• mean must be a valid pointer or null.
• variance must be a valid pointer or null.
• gamma must be a valid pointer or null.
• beta must be a valid pointer or null.

pub unsafe fn setNeuronType_parameterA_parameterB( &self, neuron_type: MPSCNNNeuronType, parameter_a: c_float, parameter_b: c_float, )

👎Deprecated

Available on crate feature MPSCNNNeuronType only.

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.

Parameter neuronType: type of neuron activation function. For full list see MPSCNNNeuronType.h

Parameter parameterA: parameterA of neuron activation that is shared across all channels of convolution output.

Parameter parameterB: parameterB of neuron activation that is shared across all channels of convolution output.

pub unsafe fn neuronType(&self) -> MPSCNNNeuronType

👎Deprecated

Available on crate feature MPSCNNNeuronType only.

Getter funtion for neuronType set using setNeuronType:parameterA:parameterB method

pub unsafe fn neuronParameterA(&self) -> c_float

👎Deprecated

Getter funtion for neuronType set using setNeuronType:parameterA:parameterB method

pub unsafe fn neuronParameterB(&self) -> c_float

👎Deprecated

Getter funtion for neuronType set using setNeuronType:parameterA:parameterB method

pub unsafe fn setNeuronToPReLUWithParametersA(&self, a: &NSData)

👎Deprecated

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.

Parameter A: An array containing per-channel float values for neuron parameter A. Number of entries must be equal to outputFeatureChannels.

Methods from Deref<Target = NSObject>

pub fn doesNotRecognizeSelector(&self, sel: Sel) -> !

Handle messages the object doesn’t recognize.

See Apple’s documentation for details.

Methods from Deref<Target = AnyObject>

pub fn class(&self) -> &'static AnyClass

Dynamically find the class of this object.

Panics

May panic if the object is invalid (which may be the case for objects returned from unavailable init/new methods).

Example

Check that an instance of NSObject has the precise class NSObject.

use objc2::ClassType;
use objc2::runtime::NSObject;

let obj = NSObject::new();
assert_eq!(obj.class(), NSObject::class());

pub unsafe fn get_ivar<T>(&self, name: &str) -> &T

where T: Encode,

👎Deprecated:

this is difficult to use correctly, use Ivar::load instead.

Use Ivar::load instead.

Safety

The object must have an instance variable with the given name, and it must be of type T.

See Ivar::load_ptr for details surrounding this.

pub fn downcast_ref<T>(&self) -> Option<&T>

where T: DowncastTarget,

Attempt to downcast the object to a class of type T.

This is the reference-variant. Use Retained::downcast if you want to convert a retained object to another type.

Mutable classes

Some classes have immutable and mutable variants, such as NSString and NSMutableString.

When some Objective-C API signature says it gives you an immutable class, it generally expects you to not mutate that, even though it may technically be mutable “under the hood”.

So using this method to convert a NSString to a NSMutableString, while not unsound, is generally frowned upon unless you created the string yourself, or the API explicitly documents the string to be mutable.

See Apple’s documentation on mutability and on isKindOfClass: for more details.

Generic classes

Objective-C generics are called “lightweight generics”, and that’s because they aren’t exposed in the runtime. This makes it impossible to safely downcast to generic collections, so this is disallowed by this method.

You can, however, safely downcast to generic collections where all the type-parameters are AnyObject.

Panics

This works internally by calling isKindOfClass:. That means that the object must have the instance method of that name, and an exception will be thrown (if CoreFoundation is linked) or the process will abort if that is not the case. In the vast majority of cases, you don’t need to worry about this, since both root objects NSObject and NSProxy implement this method.

Examples

Cast an NSString back and forth from NSObject.

use objc2::rc::Retained;
use objc2_foundation::{NSObject, NSString};

let obj: Retained<NSObject> = NSString::new().into_super();
let string = obj.downcast_ref::<NSString>().unwrap();
// Or with `downcast`, if we do not need the object afterwards
let string = obj.downcast::<NSString>().unwrap();

Try (and fail) to cast an NSObject to an NSString.

use objc2_foundation::{NSObject, NSString};

let obj = NSObject::new();
assert!(obj.downcast_ref::<NSString>().is_none());

Try to cast to an array of strings.

use objc2_foundation::{NSArray, NSObject, NSString};

let arr = NSArray::from_retained_slice(&[NSObject::new()]);
// This is invalid and doesn't type check.
let arr = arr.downcast_ref::<NSArray<NSString>>();

This fails to compile, since it would require enumerating over the array to ensure that each element is of the desired type, which is a performance pitfall.

Downcast when processing each element instead.

use objc2_foundation::{NSArray, NSObject, NSString};

let arr = NSArray::from_retained_slice(&[NSObject::new()]);

for elem in arr {
    if let Some(data) = elem.downcast_ref::<NSString>() {
        // handle `data`
    }
}

Trait Implementations

impl AsRef<AnyObject> for MPSCNNSubPixelConvolutionDescriptor

fn as_ref(&self) -> &AnyObject

Converts this type into a shared reference of the (usually inferred) input type.

impl AsRef<MPSCNNConvolutionDescriptor> for MPSCNNSubPixelConvolutionDescriptor

fn as_ref(&self) -> &MPSCNNConvolutionDescriptor

Converts this type into a shared reference of the (usually inferred) input type.

impl AsRef<MPSCNNSubPixelConvolutionDescriptor> for MPSCNNSubPixelConvolutionDescriptor

fn as_ref(&self) -> &Self

Converts this type into a shared reference of the (usually inferred) input type.

impl AsRef<NSObject> for MPSCNNSubPixelConvolutionDescriptor

fn as_ref(&self) -> &NSObject

Converts this type into a shared reference of the (usually inferred) input type.

impl Borrow<AnyObject> for MPSCNNSubPixelConvolutionDescriptor

fn borrow(&self) -> &AnyObject

Immutably borrows from an owned value.

impl Borrow<MPSCNNConvolutionDescriptor> for MPSCNNSubPixelConvolutionDescriptor

fn borrow(&self) -> &MPSCNNConvolutionDescriptor

Immutably borrows from an owned value.

impl Borrow<NSObject> for MPSCNNSubPixelConvolutionDescriptor

fn borrow(&self) -> &NSObject

Immutably borrows from an owned value.

impl ClassType for MPSCNNSubPixelConvolutionDescriptor

const NAME: &'static str = "MPSCNNSubPixelConvolutionDescriptor"

The name of the Objective-C class that this type represents.

type Super = MPSCNNConvolutionDescriptor

The superclass of this class.

type ThreadKind = <<MPSCNNSubPixelConvolutionDescriptor as ClassType>::Super as ClassType>::ThreadKind

Whether the type can be used from any thread, or from only the main thread.

fn class() -> &'static AnyClass

Get a reference to the Objective-C class that this type represents.

fn as_super(&self) -> &Self::Super

Get an immutable reference to the superclass.

impl CopyingHelper for MPSCNNSubPixelConvolutionDescriptor

type Result = MPSCNNSubPixelConvolutionDescriptor

The immutable counterpart of the type, or Self if the type has no immutable counterpart.

impl Debug for MPSCNNSubPixelConvolutionDescriptor

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Deref for MPSCNNSubPixelConvolutionDescriptor

type Target = MPSCNNConvolutionDescriptor

The resulting type after dereferencing.

fn deref(&self) -> &Self::Target

Dereferences the value.

impl Hash for MPSCNNSubPixelConvolutionDescriptor

fn hash<H: Hasher>(&self, state: &mut H)

Feeds this value into the given Hasher.

fn hash_slice<H>(data: &[Self], state: &mut H)

where H: Hasher, Self: Sized,

Feeds a slice of this type into the given Hasher.

impl Message for MPSCNNSubPixelConvolutionDescriptor

fn retain(&self) -> Retained<Self>

where Self: Sized,

Increment the reference count of the receiver.

impl NSCoding for MPSCNNSubPixelConvolutionDescriptor

unsafe fn encodeWithCoder(&self, coder: &NSCoder)

where Self: Sized + Message,

Available on crate feature NSCoder only.

Safety

unsafe fn initWithCoder( this: Allocated<Self>, coder: &NSCoder, ) -> Option<Retained<Self>>

where Self: Sized + Message,

Available on crate feature NSCoder only.

Safety

impl NSCopying for MPSCNNSubPixelConvolutionDescriptor

fn copy(&self) -> Retained<Self::Result>

where Self: Sized + Message + CopyingHelper,

Returns a new instance that’s a copy of the receiver.

unsafe fn copyWithZone(&self, zone: *mut NSZone) -> Retained<Self::Result>

where Self: Sized + Message + CopyingHelper,

Returns a new instance that’s a copy of the receiver.

impl NSObjectProtocol for MPSCNNSubPixelConvolutionDescriptor

fn isEqual(&self, other: Option<&AnyObject>) -> bool

where Self: Sized + Message,

Check whether the object is equal to an arbitrary other object.

fn hash(&self) -> usize

where Self: Sized + Message,

An integer that can be used as a table address in a hash table structure.

fn isKindOfClass(&self, cls: &AnyClass) -> bool

where Self: Sized + Message,

Check if the object is an instance of the class, or one of its subclasses.

fn is_kind_of<T>(&self) -> bool

where T: ClassType, Self: Sized + Message,

👎Deprecated:

use isKindOfClass directly, or cast your objects with AnyObject::downcast_ref

Check if the object is an instance of the class type, or one of its subclasses.

fn isMemberOfClass(&self, cls: &AnyClass) -> bool

where Self: Sized + Message,

Check if the object is an instance of a specific class, without checking subclasses.

fn respondsToSelector(&self, aSelector: Sel) -> bool

where Self: Sized + Message,

Check whether the object implements or inherits a method with the given selector.

fn conformsToProtocol(&self, aProtocol: &AnyProtocol) -> bool

where Self: Sized + Message,

Check whether the object conforms to a given protocol.

fn description(&self) -> Retained<NSObject>

where Self: Sized + Message,

A textual representation of the object.

fn debugDescription(&self) -> Retained<NSObject>

where Self: Sized + Message,

A textual representation of the object to use when debugging.

fn isProxy(&self) -> bool

where Self: Sized + Message,

Check whether the receiver is a subclass of the NSProxy root class instead of the usual NSObject.

fn retainCount(&self) -> usize

where Self: Sized + Message,

The reference count of the object.

impl NSSecureCoding for MPSCNNSubPixelConvolutionDescriptor

fn supportsSecureCoding() -> bool

where Self: Sized + ClassType,

impl PartialEq for MPSCNNSubPixelConvolutionDescriptor

fn eq(&self, other: &Self) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl RefEncode for MPSCNNSubPixelConvolutionDescriptor

const ENCODING_REF: Encoding = <MPSCNNConvolutionDescriptor as ::objc2::RefEncode>::ENCODING_REF

The Objective-C type-encoding for a reference of this type.

impl DowncastTarget for MPSCNNSubPixelConvolutionDescriptor

impl Eq for MPSCNNSubPixelConvolutionDescriptor