[−][src]Trait opencv::hub_prelude::LayerTrait

pub trait LayerTrait: AlgorithmTrait {
    fn as_raw_Layer(&self) -> *mut c_void;

    fn blobs(&mut self) -> VectorOfMat { ... }
    fn set_blobs(&mut self, val: VectorOfMat) { ... }
    fn name(&self) -> String { ... }
    fn set_name(&mut self, val: &str) { ... }
    fn typ(&self) -> String { ... }
    fn set_type(&mut self, val: &str) { ... }
    fn preferable_target(&self) -> i32 { ... }
    fn set_preferable_target(&mut self, val: i32) { ... }
    fn finalize(
        &mut self, 
        inputs: &dyn ToInputArray, 
        outputs: &mut dyn ToOutputArray
    ) -> Result<()> { ... }
    fn forward_mat(
        &mut self, 
        input: &mut VectorOfMat, 
        output: &mut VectorOfMat, 
        internals: &mut VectorOfMat
    ) -> Result<()> { ... }
    fn forward(
        &mut self, 
        inputs: &dyn ToInputArray, 
        outputs: &mut dyn ToOutputArray, 
        internals: &mut dyn ToOutputArray
    ) -> Result<()> { ... }
    fn forward_fallback(
        &mut self, 
        inputs: &dyn ToInputArray, 
        outputs: &mut dyn ToOutputArray, 
        internals: &mut dyn ToOutputArray
    ) -> Result<()> { ... }
    fn finalize_mat_to(
        &mut self, 
        inputs: &VectorOfMat, 
        outputs: &mut VectorOfMat
    ) -> Result<()> { ... }
    fn finalize_mat(&mut self, inputs: &VectorOfMat) -> Result<VectorOfMat> { ... }
    fn run(
        &mut self, 
        inputs: &VectorOfMat, 
        outputs: &mut VectorOfMat, 
        internals: &mut VectorOfMat
    ) -> Result<()> { ... }
    fn input_name_to_index(&mut self, input_name: &str) -> Result<i32> { ... }
    fn output_name_to_index(&mut self, output_name: &str) -> Result<i32> { ... }
    fn support_backend(&mut self, backend_id: i32) -> Result<bool> { ... }
    fn init_halide(
        &mut self, 
        inputs: &VectorOfPtrOfBackendWrapper
    ) -> Result<PtrOfBackendNode> { ... }
    fn init_inf_engine(
        &mut self, 
        inputs: &VectorOfPtrOfBackendWrapper
    ) -> Result<PtrOfBackendNode> { ... }
    fn init_ngraph(
        &mut self, 
        inputs: &VectorOfPtrOfBackendWrapper, 
        nodes: &VectorOfPtrOfBackendNode
    ) -> Result<PtrOfBackendNode> { ... }
    fn init_vk_com(
        &mut self, 
        inputs: &VectorOfPtrOfBackendWrapper
    ) -> Result<PtrOfBackendNode> { ... }
    fn init_cuda(
        &mut self, 
        context: *mut c_void, 
        inputs: &VectorOfPtrOfBackendWrapper, 
        outputs: &VectorOfPtrOfBackendWrapper
    ) -> Result<PtrOfBackendNode> { ... }
    fn apply_halide_scheduler(
        &self, 
        node: &mut PtrOfBackendNode, 
        inputs: &VectorOfMat, 
        outputs: &VectorOfMat, 
        target_id: i32
    ) -> Result<()> { ... }
    fn try_attach(
        &mut self, 
        node: &PtrOfBackendNode
    ) -> Result<PtrOfBackendNode> { ... }
    fn set_activation(&mut self, layer: &PtrOfActivationLayer) -> Result<bool> { ... }
    fn try_fuse(&mut self, top: &mut PtrOfLayer) -> Result<bool> { ... }
    fn get_scale_shift(&self, scale: &mut Mat, shift: &mut Mat) -> Result<()> { ... }
    fn unset_attached(&mut self) -> Result<()> { ... }
    fn get_memory_shapes(
        &self, 
        inputs: &VectorOfMatShape, 
        required_outputs: i32, 
        outputs: &mut VectorOfMatShape, 
        internals: &mut VectorOfMatShape
    ) -> Result<bool> { ... }
    fn get_flops(
        &self, 
        inputs: &VectorOfMatShape, 
        outputs: &VectorOfMatShape
    ) -> Result<i64> { ... }
    fn set_params_from(&mut self, params: &LayerParams) -> Result<()> { ... }
}

This interface class allows to build new Layers - are building blocks of networks.

Each class, derived from Layer, must implement allocate() methods to declare own outputs and forward() to compute outputs. Also before using the new layer into networks you must register your layer by using one of @ref dnnLayerFactory "LayerFactory" macros.

Required methods

`fn as_raw_Layer(&self) -> *mut c_void`

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Provided methods

`fn blobs(&mut self) -> VectorOfMat`

List of learned parameters must be stored here to allow read them by using Net::getParam().

`fn set_blobs(&mut self, val: VectorOfMat)`

List of learned parameters must be stored here to allow read them by using Net::getParam().

`fn name(&self) -> String`

Name of the layer instance, can be used for logging or other internal purposes.

`fn set_name(&mut self, val: &str)`

Name of the layer instance, can be used for logging or other internal purposes.

`fn typ(&self) -> String`

Type name which was used for creating layer by layer factory.

`fn set_type(&mut self, val: &str)`

Type name which was used for creating layer by layer factory.

`fn preferable_target(&self) -> i32`

prefer target for layer forwarding

`fn set_preferable_target(&mut self, val: i32)`

prefer target for layer forwarding

`fn finalize( &mut self, inputs: &dyn ToInputArray, outputs: &mut dyn ToOutputArray ) -> Result<()>`

Computes and sets internal parameters according to inputs, outputs and blobs.

Parameters

inputs: vector of already allocated input blobs
outputs:[out] vector of already allocated output blobs

If this method is called after network has allocated all memory for input and output blobs and before inferencing.

`fn forward_mat( &mut self, input: &mut VectorOfMat, output: &mut VectorOfMat, internals: &mut VectorOfMat ) -> Result<()>`

Deprecated:

Use Layer::forward(InputArrayOfArrays, OutputArrayOfArrays, OutputArrayOfArrays) instead

Given the @p input blobs, computes the output @p blobs.

Deprecated: Use Layer::forward(InputArrayOfArrays, OutputArrayOfArrays, OutputArrayOfArrays) instead

Parameters

input: the input blobs.
output:[out] allocated output blobs, which will store results of the computation.
internals:[out] allocated internal blobs

`fn forward( &mut self, inputs: &dyn ToInputArray, outputs: &mut dyn ToOutputArray, internals: &mut dyn ToOutputArray ) -> Result<()>`

Given the @p input blobs, computes the output @p blobs.

Parameters

inputs: the input blobs.
outputs:[out] allocated output blobs, which will store results of the computation.
internals:[out] allocated internal blobs

`fn forward_fallback( &mut self, inputs: &dyn ToInputArray, outputs: &mut dyn ToOutputArray, internals: &mut dyn ToOutputArray ) -> Result<()>`

Given the @p input blobs, computes the output @p blobs.

Parameters

inputs: the input blobs.
outputs:[out] allocated output blobs, which will store results of the computation.
internals:[out] allocated internal blobs

`fn finalize_mat_to( &mut self, inputs: &VectorOfMat, outputs: &mut VectorOfMat ) -> Result<()>`

Deprecated:

Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead

@brief Computes and sets internal parameters according to inputs, outputs and blobs.

Parameters

inputs: vector of already allocated input blobs
outputs:[out] vector of already allocated output blobs

If this method is called after network has allocated all memory for input and output blobs and before inferencing.

Overloaded parameters

Deprecated: Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead

`fn finalize_mat(&mut self, inputs: &VectorOfMat) -> Result<VectorOfMat>`

Deprecated:

Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead

@brief Computes and sets internal parameters according to inputs, outputs and blobs.

Parameters

inputs: vector of already allocated input blobs
outputs:[out] vector of already allocated output blobs

If this method is called after network has allocated all memory for input and output blobs and before inferencing.

Overloaded parameters

Deprecated: Use Layer::finalize(InputArrayOfArrays, OutputArrayOfArrays) instead

`fn run( &mut self, inputs: &VectorOfMat, outputs: &mut VectorOfMat, internals: &mut VectorOfMat ) -> Result<()>`

Deprecated:

This method will be removed in the future release.

Allocates layer and computes output.

Deprecated: This method will be removed in the future release.

`fn input_name_to_index(&mut self, input_name: &str) -> Result<i32>`

Returns index of input blob into the input array.

Parameters

inputName: label of input blob

Each layer input and output can be labeled to easily identify them using "%<layer_name%>[.output_name]" notation. This method maps label of input blob to its index into input vector.

`fn output_name_to_index(&mut self, output_name: &str) -> Result<i32>`

Returns index of output blob in output array.

Parameters

backendId: computation backend identifier.

Parameters

inputs: Input Halide buffers.

Parameters

context: void pointer to CSLContext object
inputs: layer inputs
outputs: layer outputs

`fn apply_halide_scheduler( &self, node: &mut PtrOfBackendNode, inputs: &VectorOfMat, outputs: &VectorOfMat, target_id: i32 ) -> Result<()>`

Automatic Halide scheduling based on layer hyper-parameters.

Parameters

node: Backend node with Halide functions.
inputs: Blobs that will be used in forward invocations.
outputs: Blobs that will be used in forward invocations.
targetId: Target identifier

Parameters

node: Backend node of bottom layer.

Parameters

layer: The subsequent activation layer.

Returns true if the activation layer has been attached successfully.

`fn try_fuse(&mut self, top: &mut PtrOfLayer) -> Result<bool>`

Try to fuse current layer with a next one

Parameters

top: Next layer to be fused.

Returns

True if fusion was performed.

`fn get_scale_shift(&self, scale: &mut Mat, shift: &mut Mat) -> Result<()>`

Returns parameters of layers with channel-wise multiplication and addition.

Parameters

scale:[out] Channel-wise multipliers. Total number of values should be equal to number of channels.
shift:[out] Channel-wise offsets. Total number of values should be equal to number of channels.

Some layers can fuse their transformations with further layers. In example, convolution + batch normalization. This way base layer use weights from layer after it. Fused layer is skipped. By default, @p scale and @p shift are empty that means layer has no element-wise multiplications or additions.