Trait opencv::dnn::NetTrait[−][src]

pub trait NetTrait: NetTraitConst {

Show 28 methods    fn as_raw_mut_Net(&mut self) -> *mut c_void;

    fn dump(&mut self) -> Result<String> { ... }

    fn dump_to_file(&mut self, path: &str) -> Result<()> { ... }

    fn add_layer_type(
        &mut self, 
        name: &str, 
        typ: &str, 
        dtype: &i32, 
        params: &mut LayerParams
    ) -> Result<i32> { ... }

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

    fn add_layer_to_prev_type(
        &mut self, 
        name: &str, 
        typ: &str, 
        dtype: &i32, 
        params: &mut LayerParams
    ) -> Result<i32> { ... }

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

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

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

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

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

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

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

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

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

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

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

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

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

    fn quantize(
        &mut self, 
        calib_data: &dyn ToInputArray, 
        inputs_dtype: i32, 
        outputs_dtype: i32
    ) -> Result<Net> { ... }

    fn set_halide_scheduler(&mut self, scheduler: &str) -> Result<()> { ... }

    fn set_preferable_backend(&mut self, backend_id: i32) -> Result<()> { ... }

    fn set_preferable_target(&mut self, target_id: i32) -> Result<()> { ... }

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

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

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

    fn enable_fusion(&mut self, fusion: bool) -> Result<()> { ... }

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

}

Required methods

Provided methods

Dump net to String

Returns

String with structure, hyperparameters, backend, target and fusion Call method after setInput(). To see correct backend, target and fusion run after forward().

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

Parameters
  • path: path to output file with .dot extension
See also

dump()

Adds new layer to the net.

Parameters
  • name: unique name of the adding layer.
  • type: typename of the adding layer (type must be registered in LayerRegister).
  • dtype: datatype of output blobs.
  • params: parameters which will be used to initialize the creating layer.
Returns

unique identifier of created layer, or -1 if a failure will happen.

Adds new layer to the net.

Parameters
  • name: unique name of the adding layer.
  • type: typename of the adding layer (type must be registered in LayerRegister).
  • dtype: datatype of output blobs.
  • params: parameters which will be used to initialize the creating layer.
Returns

unique identifier of created layer, or -1 if a failure will happen.

Overloaded parameters

Datatype of output blobs set to default CV_32F

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

See also

addLayer()

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

See also

addLayer()

Overloaded parameters

Converts string name of the layer to the integer identifier.

Returns

id of the layer, or -1 if the layer wasn’t found.

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

Returns pointers to input layers of specific layer.

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

Parameters
  • outPin: descriptor of the first layer output.
  • inpPin: descriptor of the second layer input.

Descriptors have the following template <layer_name>[.input_number]:

  • the first part of the template layer_name is string name of the added layer. If this part is empty then the network input pseudo layer will be used;
  • the second optional part of the template input_number is either number of the layer input, either label one. If this part is omitted then the first layer input will be used.
See also

setNetInputs(), Layer::inputNameToIndex(), Layer::outputNameToIndex()

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

Parameters
  • outLayerId: identifier of the first layer
  • outNum: number of the first layer output
  • inpLayerId: identifier of the second layer
  • inpNum: number of the second layer input

Sets outputs names of the network input pseudo layer.

Each net always has special own the network input pseudo layer with id=0. This layer stores the user blobs only and don’t make any computations. In fact, this layer provides the only way to pass user data into the network. As any other layer, this layer can label its outputs and this function provides an easy way to do this.

Specify shape of network input.

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

Parameters
  • outputName: name for layer which output is needed to get
Returns

blob for first output of specified layer. @details By default runs forward pass for the whole network.

C++ default parameters
  • output_name: String()

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

Parameters
  • outputName: name for layer which output is needed to get @details By default runs forward pass for the whole network.

This is an asynchronous version of forward(const String&). dnn::DNN_BACKEND_INFERENCE_ENGINE backend is required.

C++ default parameters
  • output_name: String()

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

Parameters
  • outputBlobs: contains all output blobs for specified layer.
  • outputName: name for layer which output is needed to get @details If @p outputName is empty, runs forward pass for the whole network.
C++ default parameters
  • output_name: String()

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

Parameters
  • outputBlobs: contains blobs for first outputs of specified layers.
  • outBlobNames: names for layers which outputs are needed to get

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

Parameters
  • outputBlobs: contains all output blobs for each layer specified in @p outBlobNames.
  • outBlobNames: names for layers which outputs are needed to get

Returns a quantized Net from a floating-point Net.

Parameters
  • calibData: Calibration data to compute the quantization parameters.
  • inputsDtype: Datatype of quantized net’s inputs. Can be CV_32F or CV_8S.
  • outputsDtype: Datatype of quantized net’s outputs. Can be CV_32F or CV_8S.

Compile Halide layers.

Parameters
  • scheduler: Path to YAML file with scheduling directives.
See also

setPreferableBackend

Schedule layers that support Halide backend. Then compile them for specific target. For layers that not represented in scheduling file or if no manual scheduling used at all, automatic scheduling will be applied.

Ask network to use specific computation backend where it supported.

Parameters
  • backendId: backend identifier.
See also

Backend

If OpenCV is compiled with Intel’s Inference Engine library, DNN_BACKEND_DEFAULT means DNN_BACKEND_INFERENCE_ENGINE. Otherwise it equals to DNN_BACKEND_OPENCV.

Ask network to make computations on specific target device.

Parameters
  • targetId: target identifier.
See also

Target

List of supported combinations backend / target: | | DNN_BACKEND_OPENCV | DNN_BACKEND_INFERENCE_ENGINE | DNN_BACKEND_HALIDE | DNN_BACKEND_CUDA | |————————|––––––––––|——————————|––––––––––|—————––| | DNN_TARGET_CPU | + | + | + | | | DNN_TARGET_OPENCL | + | + | + | | | DNN_TARGET_OPENCL_FP16 | + | + | | | | DNN_TARGET_MYRIAD | | + | | | | DNN_TARGET_FPGA | | + | | | | DNN_TARGET_CUDA | | | | + | | DNN_TARGET_CUDA_FP16 | | | | + | | DNN_TARGET_HDDL | | + | | |

Sets the new input value for the network

Parameters
  • blob: A new blob. Should have CV_32F or CV_8U depth.
  • name: A name of input layer.
  • scalefactor: An optional normalization scale.
  • mean: An optional mean subtraction values.
See also

connect(String, String) to know format of the descriptor.

If scale or mean values are specified, a final input blob is computed as: block formula

C++ default parameters
  • name: “”
  • scalefactor: 1.0
  • mean: Scalar()

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

Parameters
  • layer: name or id of the layer.
  • numParam: index of the layer parameter in the Layer::blobs array.
  • blob: the new value.
See also

Layer::blobs

Note: If shape of the new blob differs from the previous shape, then the following forward pass may fail.

Returns parameter blob of the layer.

Parameters
  • layer: name or id of the layer.
  • numParam: index of the layer parameter in the Layer::blobs array.
See also

Layer::blobs

C++ default parameters
  • num_param: 0

Enables or disables layer fusion in the network.

Parameters
  • fusion: true to enable the fusion, false to disable. The fusion is enabled by default.

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. Supported by DNN_BACKEND_OPENCV on DNN_TARGET_CPU only.

Parameters
  • timings:[out] vector for tick timings for all layers.
Returns

overall ticks for model inference.

Implementors