Struct MPSCNNBinaryFullyConnectedNode 

pub struct MPSCNNBinaryFullyConnectedNode { /* private fields */ }

Available on crate feature MPSNNGraphNodes only.

A MPSNNFilterNode representing a MPSCNNBinaryFullyConnected kernel

See also Apple’s documentation

Implementations

impl MPSCNNBinaryFullyConnectedNode

pub unsafe fn nodeWithSource_weights_scaleValue_type_flags( source_node: &MPSNNImageNode, weights: &ProtocolObject<dyn MPSCNNConvolutionDataSource>, scale_value: c_float, type: MPSCNNBinaryConvolutionType, flags: MPSCNNBinaryConvolutionFlags, ) -> Retained<Self>

Available on crate features MPSNeuralNetwork and MPSCNNConvolution and MPSNeuralNetworkTypes only.

Init an autoreleased node representing a MPSCNNBinaryFullyConnected kernel

Parameter sourceNode: The MPSNNImageNode representing the source MPSImage for the filter

Parameter weights: A pointer to a valid object conforming to the MPSCNNConvolutionDataSource protocol. This object is provided by you to encapsulate storage for convolution weights and biases.

Parameter scaleValue: A floating point value used to scale the entire convolution.

Parameter type: What kind of binarization strategy is to be used.

Parameter flags: See documentation of MPSCNNBinaryConvolutionFlags.

Returns: A new MPSNNFilter node for a MPSCNNBinaryFullyConnected kernel.

pub unsafe fn initWithSource_weights_scaleValue_type_flags( this: Allocated<Self>, source_node: &MPSNNImageNode, weights: &ProtocolObject<dyn MPSCNNConvolutionDataSource>, scale_value: c_float, type: MPSCNNBinaryConvolutionType, flags: MPSCNNBinaryConvolutionFlags, ) -> Retained<Self>

Available on crate features MPSNeuralNetwork and MPSCNNConvolution and MPSNeuralNetworkTypes only.

Init a node representing a MPSCNNBinaryFullyConnected kernel

Parameter sourceNode: The MPSNNImageNode representing the source MPSImage for the filter

Parameter weights: A pointer to a valid object conforming to the MPSCNNConvolutionDataSource protocol. This object is provided by you to encapsulate storage for convolution weights and biases.

Parameter scaleValue: A floating point value used to scale the entire convolution.

Parameter type: What kind of binarization strategy is to be used.

Parameter flags: See documentation of MPSCNNBinaryConvolutionFlags.

Returns: A new MPSNNFilter node for a MPSCNNBinaryFullyConnected kernel.

pub unsafe fn nodeWithSource_weights_outputBiasTerms_outputScaleTerms_inputBiasTerms_inputScaleTerms_type_flags( source_node: &MPSNNImageNode, weights: &ProtocolObject<dyn MPSCNNConvolutionDataSource>, output_bias_terms: *const c_float, output_scale_terms: *const c_float, input_bias_terms: *const c_float, input_scale_terms: *const c_float, type: MPSCNNBinaryConvolutionType, flags: MPSCNNBinaryConvolutionFlags, ) -> Retained<Self>

Available on crate features MPSNeuralNetwork and MPSCNNConvolution and MPSNeuralNetworkTypes only.

Init an autoreleased node representing a MPSCNNBinaryFullyConnected kernel

Parameter sourceNode: The MPSNNImageNode representing the source MPSImage for the filter

Parameter weights: A pointer to a valid object conforming to the MPSCNNConvolutionDataSource protocol. This object is provided by you to encapsulate storage for convolution weights and biases.

Parameter outputBiasTerms: A pointer to bias terms to be applied to the convolution output. See MPSCNNBinaryConvolution for more details.

Parameter outputScaleTerms: A pointer to scale terms to be applied to binary convolution results per output feature channel. See MPSCNNBinaryConvolution for more details.

Parameter inputBiasTerms: A pointer to offset terms to be applied to the input before convolution and before input scaling. See MPSCNNBinaryConvolution for more details.

Parameter inputScaleTerms: A pointer to scale terms to be applied to the input before convolution, but after input biasing. See MPSCNNBinaryConvolution for more details.

Parameter type: What kind of binarization strategy is to be used.

Parameter flags: See documentation of MPSCNNBinaryConvolutionFlags.

Returns: A new MPSNNFilter node for a MPSCNNBinaryFullyConnected kernel.

Safety

• output_bias_terms must be a valid pointer or null.
• output_scale_terms must be a valid pointer or null.
• input_bias_terms must be a valid pointer or null.
• input_scale_terms must be a valid pointer or null.

pub unsafe fn initWithSource_weights_outputBiasTerms_outputScaleTerms_inputBiasTerms_inputScaleTerms_type_flags( this: Allocated<Self>, source_node: &MPSNNImageNode, weights: &ProtocolObject<dyn MPSCNNConvolutionDataSource>, output_bias_terms: *const c_float, output_scale_terms: *const c_float, input_bias_terms: *const c_float, input_scale_terms: *const c_float, type: MPSCNNBinaryConvolutionType, flags: MPSCNNBinaryConvolutionFlags, ) -> Retained<Self>

Available on crate features MPSNeuralNetwork and MPSCNNConvolution and MPSNeuralNetworkTypes only.

Init a node representing a MPSCNNBinaryFullyConnected kernel

Parameter sourceNode: The MPSNNImageNode representing the source MPSImage for the filter

Parameter weights: A pointer to a valid object conforming to the MPSCNNConvolutionDataSource protocol. This object is provided by you to encapsulate storage for convolution weights and biases.

Parameter outputBiasTerms: A pointer to bias terms to be applied to the convolution output. See MPSCNNBinaryConvolution for more details.

Parameter outputScaleTerms: A pointer to scale terms to be applied to binary convolution results per output feature channel. See MPSCNNBinaryConvolution for more details.

Parameter inputBiasTerms: A pointer to offset terms to be applied to the input before convolution and before input scaling. See MPSCNNBinaryConvolution for more details.

Parameter inputScaleTerms: A pointer to scale terms to be applied to the input before convolution, but after input biasing. See MPSCNNBinaryConvolution for more details.

Parameter type: What kind of binarization strategy is to be used.

Parameter flags: See documentation of MPSCNNBinaryConvolutionFlags.

Returns: A new MPSNNFilter node for a MPSCNNBinaryFullyConnected kernel.

Safety

• output_bias_terms must be a valid pointer or null.
• output_scale_terms must be a valid pointer or null.
• input_bias_terms must be a valid pointer or null.
• input_scale_terms must be a valid pointer or null.

impl MPSCNNBinaryFullyConnectedNode

Methods declared on superclass MPSCNNConvolutionNode.

pub unsafe fn nodeWithSource_weights( source_node: &MPSNNImageNode, weights: &ProtocolObject<dyn MPSCNNConvolutionDataSource>, ) -> Retained<Self>

Available on crate features MPSNeuralNetwork and MPSCNNConvolution only.

Init an autoreleased not representing a MPSCNNConvolution kernel

Parameter sourceNode: The MPSNNImageNode representing the source MPSImage for the filter

Parameter weights: A pointer to a valid object conforming to the MPSCNNConvolutionDataSource protocol. This object is provided by you to encapsulate storage for convolution weights and biases. If it is used for training, it may not have a neuron embedded in the convolution descriptor.

Returns: A new MPSNNFilter node for a MPSCNNConvolution kernel.

pub unsafe fn initWithSource_weights( this: Allocated<Self>, source_node: &MPSNNImageNode, weights: &ProtocolObject<dyn MPSCNNConvolutionDataSource>, ) -> Retained<Self>

Available on crate features MPSNeuralNetwork and MPSCNNConvolution only.

Init a node representing a MPSCNNConvolution kernel

Parameter sourceNode: The MPSNNImageNode representing the source MPSImage for the filter

Parameter weights: A pointer to a valid object conforming to the MPSCNNConvolutionDataSource protocol. This object is provided by you to encapsulate storage for convolution weights and biases. If it is used for training, it may not have a neuron embedded in the convolution descriptor.

Returns: A new MPSNNFilter node for a MPSCNNConvolution kernel.

impl MPSCNNBinaryFullyConnectedNode

Methods declared on superclass MPSNNFilterNode.

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

Available on crate feature MPSNeuralNetwork only.

impl MPSCNNBinaryFullyConnectedNode

Methods declared on superclass NSObject.

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

Available on crate feature MPSNeuralNetwork only.

Methods from Deref<Target = MPSCNNBinaryConvolutionNode>

pub unsafe fn convolutionGradientState( &self, ) -> Option<Retained<MPSCNNConvolutionGradientStateNode>>

Available on crate feature MPSNeuralNetwork only.

unavailable

Methods from Deref<Target = MPSCNNConvolutionNode>

pub unsafe fn trainingStyle(&self) -> MPSNNTrainingStyle

Available on crate features MPSNeuralNetwork and MPSNeuralNetworkTypes only.

The training style of the forward node will be propagated to gradient nodes made from it

pub unsafe fn setTrainingStyle(&self, training_style: MPSNNTrainingStyle)

Available on crate features MPSNeuralNetwork and MPSNeuralNetworkTypes only.

Setter for trainingStyle.

pub unsafe fn accumulatorPrecision( &self, ) -> MPSNNConvolutionAccumulatorPrecisionOption

Available on crate features MPSNeuralNetwork and MPSNeuralNetworkTypes only.

Set the floating-point precision used by the convolution accumulator

Default: MPSNNConvolutionAccumulatorPrecisionOptionFloat

pub unsafe fn setAccumulatorPrecision( &self, accumulator_precision: MPSNNConvolutionAccumulatorPrecisionOption, )

Available on crate features MPSNeuralNetwork and MPSNeuralNetworkTypes only.

Setter for accumulatorPrecision.

pub unsafe fn convolutionGradientState( &self, ) -> Option<Retained<MPSCNNConvolutionGradientStateNode>>

Available on crate feature MPSNeuralNetwork only.

A node to represent a MPSCNNConvolutionGradientState object

Use this if the convolution is mirrored by a convolution transpose node later on in the graph to make sure that the size of the image returned from the convolution transpose matches the size of the image passed in to this node.

Methods from Deref<Target = MPSNNFilterNode>

pub unsafe fn resultImage(&self) -> Retained<MPSNNImageNode>

Available on crate feature MPSNeuralNetwork only.

Get the node representing the image result of the filter

Except where otherwise noted, the precision used for the result image (see format property) is copied from the precision from the first input image node.

pub unsafe fn resultState(&self) -> Option<Retained<MPSNNStateNode>>

Available on crate feature MPSNeuralNetwork only.

convenience method for resultStates[0]

If resultStates is nil, returns nil

pub unsafe fn resultStates(&self) -> Option<Retained<NSArray<MPSNNStateNode>>>

Available on crate feature MPSNeuralNetwork only.

Get the node representing the state result of the filter

If more than one, see description of subclass for ordering.

pub unsafe fn paddingPolicy(&self) -> Retained<ProtocolObject<dyn MPSNNPadding>>

Available on crate features MPSNeuralNetwork and MPSNeuralNetworkTypes only.

The padding method used for the filter node

The padding policy configures how the filter centers the region of interest in the source image. It principally is responsible for setting the MPSCNNKernel.offset and the size of the image produced, and sometimes will also configure .sourceFeatureChannelOffset, .sourceFeatureChannelMaxCount, and .edgeMode. It is permitted to set any other filter properties as needed using a custom padding policy. The default padding policy varies per filter to conform to consensus expectation for the behavior of that filter. In some cases, pre-made padding policies are provided to match the behavior of common neural networking frameworks with particularly complex or unexpected behavior for specific nodes. See MPSNNDefaultPadding class methods in MPSNeuralNetworkTypes.h for more.

BUG: MPS doesn’t provide a good way to reset the MPSKernel properties in the context of a MPSNNGraph after the kernel is finished encoding. These values carry on to the next time the graph is used. Consequently, if your custom padding policy modifies the property as a function of the previous value, e.g.:

kernel.someProperty += 2;

then the second time the graph runs, the property may have an inconsistent value, leading to unexpected behavior. The default padding computation runs before the custom padding method to provide it with a sense of what is expected for the default configuration and will reinitialize the value in the case of the .offset. However, that computation usually doesn’t reset other properties. In such cases, the custom padding policy may need to keep a record of the original value to enable consistent behavior.

pub unsafe fn setPaddingPolicy( &self, padding_policy: &ProtocolObject<dyn MPSNNPadding>, )

Available on crate features MPSNeuralNetwork and MPSNeuralNetworkTypes only.

Setter for paddingPolicy.

pub unsafe fn label(&self) -> Option<Retained<NSString>>

Available on crate feature MPSNeuralNetwork only.

A string to help identify this object.

pub unsafe fn setLabel(&self, label: Option<&NSString>)

Available on crate feature MPSNeuralNetwork only.

Setter for label.

This is copied when set.

pub unsafe fn gradientFilterWithSource( &self, gradient_image: &MPSNNImageNode, ) -> Retained<MPSNNGradientFilterNode>

Available on crate feature MPSNeuralNetwork only.

Return the gradient (backwards) version of this filter.

The backwards training version of the filter will be returned. The non-gradient image and state arguments for the filter are automatically obtained from the target.

Parameter gradientImage: The gradient images corresponding with the resultImage of the target

pub unsafe fn gradientFilterWithSources( &self, gradient_images: &NSArray<MPSNNImageNode>, ) -> Retained<MPSNNGradientFilterNode>

Available on crate feature MPSNeuralNetwork only.

Return the gradient (backwards) version of this filter.

The backwards training version of the filter will be returned. The non-gradient image and state arguments for the filter are automatically obtained from the target.

Parameter gradientImages: The gradient images corresponding with the resultImage of the target

pub unsafe fn gradientFiltersWithSources( &self, gradient_images: &NSArray<MPSNNImageNode>, ) -> Retained<NSArray<MPSNNGradientFilterNode>>

Available on crate feature MPSNeuralNetwork only.

Return multiple gradient versions of the filter

MPSNNFilters that consume multiple inputs generally result in multiple conjugate filters for the gradient computation at the end of training. For example, a single concatenation operation that concatenates multple images will result in an array of slice operators that carve out subsections of the input gradient image.

pub unsafe fn gradientFiltersWithSource( &self, gradient_image: &MPSNNImageNode, ) -> Retained<NSArray<MPSNNGradientFilterNode>>

Available on crate feature MPSNeuralNetwork only.

Return multiple gradient versions of the filter

MPSNNFilters that consume multiple inputs generally result in multiple conjugate filters for the gradient computation at the end of training. For example, a single concatenation operation that concatenates multple images will result in an array of slice operators that carve out subsections of the input gradient image.

pub unsafe fn trainingGraphWithSourceGradient_nodeHandler( &self, gradient_image: Option<&MPSNNImageNode>, node_handler: MPSGradientNodeBlock, ) -> Option<Retained<NSArray<MPSNNFilterNode>>>

Available on crate features MPSNeuralNetwork and block2 only.

Build training graph from inference graph

This method will iteratively build the training portion of a graph based on an inference graph. Self should be the last node in the inference graph. It is typically a loss layer, but can be anything. Typically, the “inference graph” used here is the desired inference graph with a dropout node and a loss layer node appended.

The nodes that are created will have default properties. In certain cases, these may not be appropriate (e.g. if you want to do CPU based updates of convolution weights instead of default GPU updates.) In such cases, your application should use the nodeHandler to configure the new nodes as they are created.

BUG: This method can not follow links to regions of the graph that are connected to the rest of the graph solely via MPSNNStateNodes. A gradient image input is required to construct a MPSNNGradientFilterNode from a inference filter node.

Parameter gradientImage: The input gradient image for the first gradient node in the training section of the graph. If nil, self.resultImage is used. This results in a standard monolithic training graph. If the graph is instead divided into multiple subgraphs (potentially to allow for your custom code to appear inbetween MPSNNGraph segments) a new MPSImageNode* may be substituted.

Parameter nodeHandler: An optional block to allow for customization of gradient nodes and intermediate images as the graph is constructed. It may also be used to prune braches of the developing training graph. If nil, the default handler is used. It builds the full graph, and assigns any inferenceNodeSources[i].handle to their gradient counterparts.

Returns: The list of new MPSNNFilterNode training graph termini. These MPSNNFilterNodes are not necessarily all MPSNNGradientFilterNodes. To build a full list of nodes created, use a custom nodeHandler. If no nodes are created nil is returned.

Safety

node_handler must be a valid pointer or null.

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 MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

fn as_ref(&self) -> &AnyObject

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

impl AsRef<MPSCNNBinaryConvolutionNode> for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

fn as_ref(&self) -> &MPSCNNBinaryConvolutionNode

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

impl AsRef<MPSCNNBinaryFullyConnectedNode> for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

fn as_ref(&self) -> &Self

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

impl AsRef<MPSCNNConvolutionNode> for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

fn as_ref(&self) -> &MPSCNNConvolutionNode

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

impl AsRef<MPSNNFilterNode> for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

fn as_ref(&self) -> &MPSNNFilterNode

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

impl AsRef<NSObject> for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

fn as_ref(&self) -> &NSObject

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

impl Borrow<AnyObject> for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

fn borrow(&self) -> &AnyObject

Immutably borrows from an owned value.

impl Borrow<MPSCNNBinaryConvolutionNode> for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

fn borrow(&self) -> &MPSCNNBinaryConvolutionNode

Immutably borrows from an owned value.

impl Borrow<MPSCNNConvolutionNode> for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

fn borrow(&self) -> &MPSCNNConvolutionNode

Immutably borrows from an owned value.

impl Borrow<MPSNNFilterNode> for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

fn borrow(&self) -> &MPSNNFilterNode

Immutably borrows from an owned value.

impl Borrow<NSObject> for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

fn borrow(&self) -> &NSObject

Immutably borrows from an owned value.

impl ClassType for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

const NAME: &'static str = "MPSCNNBinaryFullyConnectedNode"

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

type Super = MPSCNNBinaryConvolutionNode

The superclass of this class.

type ThreadKind = <<MPSCNNBinaryFullyConnectedNode 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 Debug for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

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

Formats the value using the given formatter.

impl Deref for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

type Target = MPSCNNBinaryConvolutionNode

The resulting type after dereferencing.

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

Dereferences the value.

impl Hash for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

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 MPSNNTrainableNode for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

unsafe fn trainingStyle(&self) -> MPSNNTrainingStyle

where Self: Sized + Message,

Available on crate feature MPSNeuralNetworkTypes only.

Configure whether and when neural network training parameters are updated

unsafe fn setTrainingStyle(&self, training_style: MPSNNTrainingStyle)

where Self: Sized + Message,

Available on crate feature MPSNeuralNetworkTypes only.

Setter for trainingStyle.

impl Message for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

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

where Self: Sized,

Increment the reference count of the receiver.

impl NSObjectProtocol for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

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 PartialEq for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

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 MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

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

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

impl DowncastTarget for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.

impl Eq for MPSCNNBinaryFullyConnectedNode

Available on crate feature MPSNeuralNetwork only.