Struct opencv::dnn::Net

pub struct Net { /* fields omitted */ }

This class allows to create and manipulate comprehensive artificial neural networks.

Neural network is presented as directed acyclic graph (DAG), where vertices are Layer instances, and edges specify relationships between layers inputs and outputs.

Each network layer has unique integer id and unique string name inside its network. LayerId can store either layer name or layer id.

This class supports reference counting of its instances, i. e. copies point to the same instance.

Implementations

impl Net

pub fn as_raw_Net(&self) -> *const c_void

pub fn as_raw_mut_Net(&mut self) -> *mut c_void

impl Net

pub fn default() -> Result<Net>

pub fn read_from_model_optimizer(xml: &str, bin: &str) -> Result<Net>

Create a network from Intel's Model Optimizer intermediate representation (IR).

Parameters

• xml: XML configuration file with network's topology.
• bin: Binary file with trained weights. Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine backend.

pub fn read_from_model_optimizer_1(
    buffer_model_config: &Vector<u8>,
    buffer_weights: &Vector<u8>
) -> Result<Net>

Create a network from Intel's Model Optimizer in-memory buffers with intermediate representation (IR).

Parameters

• bufferModelConfig: buffer with model's configuration.
• bufferWeights: buffer with model's trained weights.

Returns

Net object.

pub fn read_from_model_optimizer_2(
    buffer_model_config_ptr: &u8,
    buffer_model_config_size: size_t,
    buffer_weights_ptr: &u8,
    buffer_weights_size: size_t
) -> Result<Net>

Create a network from Intel's Model Optimizer in-memory buffers with intermediate representation (IR).

Parameters

• bufferModelConfigPtr: buffer pointer of model's configuration.
• bufferModelConfigSize: buffer size of model's configuration.
• bufferWeightsPtr: buffer pointer of model's trained weights.
• bufferWeightsSize: buffer size of model's trained weights.

Returns

Net object.

Trait Implementations

impl Boxed for Net

pub unsafe fn from_raw(ptr: *mut c_void) -> Self

Wrap the specified raw pointer

pub fn into_raw(self) -> *mut c_void

Return an the underlying raw pointer while consuming this wrapper.

pub fn as_raw(&self) -> *const c_void

Return the underlying raw pointer.

pub fn as_raw_mut(&mut self) -> *mut c_void

Return the underlying mutable raw pointer

impl Drop for Net

pub fn drop(&mut self)

Executes the destructor for this type.

impl NetTrait for Net

pub fn as_raw_Net(&self) -> *const c_void

pub fn as_raw_mut_Net(&mut self) -> *mut c_void

pub fn empty(&self) -> Result<bool>

Returns true if there are no layers in the network.

pub fn dump(&mut self) -> Result<String>

Dump net to String

pub fn dump_to_file(&mut self, path: &str) -> Result<()>

Dump net structure, hyperparameters, backend, target and fusion to dot file

pub fn add_layer(
    &mut self,
    name: &str,
    typ: &str,
    params: &mut LayerParams
) -> Result<i32>

Adds new layer to the net.

pub fn add_layer_to_prev(
    &mut self,
    name: &str,
    typ: &str,
    params: &mut LayerParams
) -> Result<i32>

Adds new layer and connects its first input to the first output of previously added layer.

pub fn get_layer_id(&mut self, layer: &str) -> Result<i32>

Converts string name of the layer to the integer identifier.

pub fn get_layer_names(&self) -> Result<Vector<String>>

pub fn get_layer(&mut self, layer_id: Net_LayerId) -> Result<Ptr<Layer>>

Returns pointer to layer with specified id or name which the network use.

pub fn get_layer_inputs(
    &mut self,
    layer_id: Net_LayerId
) -> Result<Vector<Ptr<Layer>>>

Returns pointers to input layers of specific layer.

pub fn connect_first_second(
    &mut self,
    out_pin: &str,
    inp_pin: &str
) -> Result<()>

Connects output of the first layer to input of the second layer.

pub fn connect(
    &mut self,
    out_layer_id: i32,
    out_num: i32,
    inp_layer_id: i32,
    inp_num: i32
) -> Result<()>

Connects #@p outNum output of the first layer to #@p inNum input of the second layer.

pub fn set_inputs_names(
    &mut self,
    input_blob_names: &Vector<String>
) -> Result<()>

Sets outputs names of the network input pseudo layer.

pub fn set_input_shape(
    &mut self,
    input_name: &str,
    shape: &MatShape
) -> Result<()>

Specify shape of network input.

pub fn forward_single(&mut self, output_name: &str) -> Result<Mat>

Runs forward pass to compute output of layer with name @p outputName.

pub fn forward_async(&mut self, output_name: &str) -> Result<AsyncArray>

Runs forward pass to compute output of layer with name @p outputName.

pub fn forward_layer(
    &mut self,
    output_blobs: &mut dyn ToOutputArray,
    output_name: &str
) -> Result<()>

Runs forward pass to compute output of layer with name @p outputName.

pub fn forward(
    &mut self,
    output_blobs: &mut dyn ToOutputArray,
    out_blob_names: &Vector<String>
) -> Result<()>

Runs forward pass to compute outputs of layers listed in @p outBlobNames.

pub fn forward_and_retrieve(
    &mut self,
    output_blobs: &mut Vector<Vector<Mat>>,
    out_blob_names: &Vector<String>
) -> Result<()>

Runs forward pass to compute outputs of layers listed in @p outBlobNames.

pub fn set_halide_scheduler(&mut self, scheduler: &str) -> Result<()>

Compile Halide layers.

pub fn set_preferable_backend(&mut self, backend_id: i32) -> Result<()>

Ask network to use specific computation backend where it supported.

pub fn set_preferable_target(&mut self, target_id: i32) -> Result<()>

Ask network to make computations on specific target device.

pub fn set_input(
    &mut self,
    blob: &dyn ToInputArray,
    name: &str,
    scalefactor: f64,
    mean: Scalar
) -> Result<()>

Sets the new input value for the network

pub fn set_param(
    &mut self,
    layer: Net_LayerId,
    num_param: i32,
    blob: &Mat
) -> Result<()>

Sets the new value for the learned param of the layer.

pub fn get_param(&mut self, layer: Net_LayerId, num_param: i32) -> Result<Mat>

Returns parameter blob of the layer.

pub fn get_unconnected_out_layers(&self) -> Result<Vector<i32>>

Returns indexes of layers with unconnected outputs.

pub fn get_unconnected_out_layers_names(&self) -> Result<Vector<String>>

Returns names of layers with unconnected outputs.

pub fn get_layers_shapes(
    &self,
    net_input_shapes: &Vector<MatShape>,
    layers_ids: &mut Vector<i32>,
    in_layers_shapes: &mut Vector<Vector<MatShape>>,
    out_layers_shapes: &mut Vector<Vector<MatShape>>
) -> Result<()>

Returns input and output shapes for all layers in loaded model; preliminary inferencing isn't necessary.

pub fn get_layers_shapes_1(
    &self,
    net_input_shape: &MatShape,
    layers_ids: &mut Vector<i32>,
    in_layers_shapes: &mut Vector<Vector<MatShape>>,
    out_layers_shapes: &mut Vector<Vector<MatShape>>
) -> Result<()>

Returns input and output shapes for all layers in loaded model; preliminary inferencing isn't necessary.

pub fn get_layer_shapes(
    &self,
    net_input_shape: &MatShape,
    layer_id: i32,
    in_layer_shapes: &mut Vector<MatShape>,
    out_layer_shapes: &mut Vector<MatShape>
) -> Result<()>

Returns input and output shapes for layer with specified id in loaded model; preliminary inferencing isn't necessary.

pub fn get_layer_shapes_1(
    &self,
    net_input_shapes: &Vector<MatShape>,
    layer_id: i32,
    in_layer_shapes: &mut Vector<MatShape>,
    out_layer_shapes: &mut Vector<MatShape>
) -> Result<()>

Returns input and output shapes for layer with specified id in loaded model; preliminary inferencing isn't necessary.

pub fn get_flops(&self, net_input_shapes: &Vector<MatShape>) -> Result<i64>

Computes FLOP for whole loaded model with specified input shapes.

pub fn get_flops_1(&self, net_input_shape: &MatShape) -> Result<i64>

Computes FLOP for whole loaded model with specified input shapes.

pub fn get_flops_2(
    &self,
    layer_id: i32,
    net_input_shapes: &Vector<MatShape>
) -> Result<i64>

Computes FLOP for whole loaded model with specified input shapes.

pub fn get_flops_3(
    &self,
    layer_id: i32,
    net_input_shape: &MatShape
) -> Result<i64>

Computes FLOP for whole loaded model with specified input shapes.

pub fn get_layer_types(&self, layers_types: &mut Vector<String>) -> Result<()>

Returns list of types for layer used in model.

pub fn get_layers_count(&self, layer_type: &str) -> Result<i32>

Returns count of layers of specified type.

pub fn get_memory_consumption(
    &self,
    net_input_shapes: &Vector<MatShape>,
    weights: &mut size_t,
    blobs: &mut size_t
) -> Result<()>

Computes bytes number which are required to store all weights and intermediate blobs for model.

pub fn get_memory_consumption_1(
    &self,
    net_input_shape: &MatShape,
    weights: &mut size_t,
    blobs: &mut size_t
) -> Result<()>

Computes bytes number which are required to store all weights and intermediate blobs for each layer.

pub fn get_memory_consumption_for_layer(
    &self,
    layer_id: i32,
    net_input_shapes: &Vector<MatShape>,
    weights: &mut size_t,
    blobs: &mut size_t
) -> Result<()>

Computes bytes number which are required to store all weights and intermediate blobs for each layer.

pub fn get_memory_consumption_2(
    &self,
    layer_id: i32,
    net_input_shape: &MatShape,
    weights: &mut size_t,
    blobs: &mut size_t
) -> Result<()>

Computes bytes number which are required to store all weights and intermediate blobs for each layer.

pub fn get_memory_consumption_for_layers(
    &self,
    net_input_shapes: &Vector<MatShape>,
    layer_ids: &mut Vector<i32>,
    weights: &mut Vector<size_t>,
    blobs: &mut Vector<size_t>
) -> Result<()>

Computes bytes number which are required to store all weights and intermediate blobs for each layer.

pub fn get_memory_consumption_3(
    &self,
    net_input_shape: &MatShape,
    layer_ids: &mut Vector<i32>,
    weights: &mut Vector<size_t>,
    blobs: &mut Vector<size_t>
) -> Result<()>

Computes bytes number which are required to store all weights and intermediate blobs for each layer.

pub fn enable_fusion(&mut self, fusion: bool) -> Result<()>

Enables or disables layer fusion in the network.

pub fn get_perf_profile(&mut self, timings: &mut Vector<f64>) -> Result<i64>

Returns overall time for inference and timings (in ticks) for layers. Indexes in returned vector correspond to layers ids. Some layers can be fused with others, in this case zero ticks count will be return for that skipped layers.

impl Send for Net