opencv::hub_prelude

Trait NetTrait

Source 
pub trait NetTrait: NetTraitConst {

Show 33 methods    // Required method
    fn as_raw_mut_Net(&mut self) -> *mut c_void;

    // Provided methods
    fn dump(&mut self) -> Result<String> { ... }
    fn dump_to_file(&mut self, path: &str) -> Result<()> { ... }
    fn dump_to_pbtxt(&mut self, path: &str) -> Result<()> { ... }
    fn add_layer_type(
        &mut self,
        name: &str,
        typ: &str,
        dtype: &i32,
        params: &mut impl LayerParamsTrait,
    ) -> Result<i32> { ... }
    fn add_layer(
        &mut self,
        name: &str,
        typ: &str,
        params: &mut impl LayerParamsTrait,
    ) -> Result<i32> { ... }
    fn add_layer_to_prev_type(
        &mut self,
        name: &str,
        typ: &str,
        dtype: &i32,
        params: &mut impl LayerParamsTrait,
    ) -> Result<i32> { ... }
    fn add_layer_to_prev(
        &mut self,
        name: &str,
        typ: &str,
        params: &mut impl LayerParamsTrait,
    ) -> Result<i32> { ... }
    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 register_output(
        &mut self,
        output_name: &str,
        layer_id: i32,
        output_port: i32,
    ) -> Result<i32> { ... }
    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_single_def(&mut self) -> Result<Mat> { ... }
    fn forward_async(&mut self, output_name: &str) -> Result<AsyncArray> { ... }
    fn forward_async_def(&mut self) -> Result<AsyncArray> { ... }
    fn forward_layer(
        &mut self,
        output_blobs: &mut impl ToOutputArray,
        output_name: &str,
    ) -> Result<()> { ... }
    fn forward_layer_def(
        &mut self,
        output_blobs: &mut impl ToOutputArray,
    ) -> Result<()> { ... }
    fn forward(
        &mut self,
        output_blobs: &mut impl 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: &impl ToInputArray,
        inputs_dtype: i32,
        outputs_dtype: i32,
        per_channel: bool,
    ) -> Result<Net> { ... }
    fn quantize_def(
        &mut self,
        calib_data: &impl 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: &impl ToInputArray,
        name: &str,
        scalefactor: f64,
        mean: Scalar,
    ) -> Result<()> { ... }
    fn set_input_def(&mut self, blob: &impl ToInputArray) -> Result<()> { ... }
    fn set_param(
        &mut self,
        layer: i32,
        num_param: i32,
        blob: &impl MatTraitConst,
    ) -> Result<()> { ... }
    fn set_param_1(
        &mut self,
        layer_name: &str,
        num_param: i32,
        blob: &impl MatTraitConst,
    ) -> Result<()> { ... }
    fn enable_fusion(&mut self, fusion: bool) -> Result<()> { ... }
    fn enable_winograd(&mut self, use_winograd: bool) -> Result<()> { ... }
    fn get_perf_profile(&mut self, timings: &mut Vector<f64>) -> Result<i64> { ... }

}
Expand description

Mutable methods for crate::dnn::Net

Required Methods§

Source

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

Provided Methods§

Source

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

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().

Source

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

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

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

dump()

Source

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

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

§Parameters
  • path: path to output file with .pbtxt extension

Use Netron (https://netron.app) to open the target file to visualize the model. Call method after setInput(). To see correct backend, target and fusion run after forward().

Source

fn add_layer_type( &mut self, name: &str, typ: &str, dtype: &i32, params: &mut impl LayerParamsTrait, ) -> Result<i32>

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.

Source

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

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

Source

fn add_layer_to_prev_type( &mut self, name: &str, typ: &str, dtype: &i32, params: &mut impl LayerParamsTrait, ) -> Result<i32>

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

§See also

addLayer()

Source

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

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

§See also

addLayer()

§Overloaded parameters
Source

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.

§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()

Source

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.

§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
Source

fn register_output( &mut self, output_name: &str, layer_id: i32, output_port: i32, ) -> Result<i32>

Registers network output with name

Function may create additional ‘Identity’ layer.

§Parameters
  • outputName: identifier of the output
  • layerId: identifier of the second layer
  • outputPort: number of the second layer input
§Returns

index of bound layer (the same as layerId or newly created)

Source

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

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.

Source

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

Specify shape of network input.

Source

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

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()
Source

fn forward_single_def(&mut self) -> Result<Mat>

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.

§Note

This alternative version of NetTrait::forward_single function uses the following default values for its arguments:

  • output_name: String()
Source

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

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()
Source

fn forward_async_def(&mut self) -> Result<AsyncArray>

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.

§Note

This alternative version of NetTrait::forward_async function uses the following default values for its arguments:

  • output_name: String()
Source

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

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()
Source

fn forward_layer_def( &mut self, output_blobs: &mut impl ToOutputArray, ) -> Result<()>

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.
§Note

This alternative version of NetTrait::forward_layer function uses the following default values for its arguments:

  • output_name: String()
Source

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

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
Source

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.

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

fn quantize( &mut self, calib_data: &impl ToInputArray, inputs_dtype: i32, outputs_dtype: i32, per_channel: bool, ) -> Result<Net>

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.
  • perChannel: Quantization granularity of quantized Net. The default is true, that means quantize model in per-channel way (channel-wise). Set it false to quantize model in per-tensor way (or tensor-wise).
§C++ default parameters
  • per_channel: true
Source

fn quantize_def( &mut self, calib_data: &impl ToInputArray, inputs_dtype: i32, outputs_dtype: i32, ) -> Result<Net>

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.
  • perChannel: Quantization granularity of quantized Net. The default is true, that means quantize model in per-channel way (channel-wise). Set it false to quantize model in per-tensor way (or tensor-wise).
§Note

This alternative version of NetTrait::quantize function uses the following default values for its arguments:

  • per_channel: true
Source

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

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.

Source

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

Ask network to use specific computation backend where it supported.

§Parameters
  • backendId: backend identifier.
§See also

Backend

Source

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

Ask network to make computations on specific target device.

§Parameters
  • targetId: target identifier.
§See also

Target

List of supported combinations backend / target:

DNN_BACKEND_OPENCVDNN_BACKEND_INFERENCE_ENGINEDNN_BACKEND_HALIDEDNN_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+
Source

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

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()
Source

fn set_input_def(&mut self, blob: &impl ToInputArray) -> Result<()>

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

§Note

This alternative version of NetTrait::set_input function uses the following default values for its arguments:

  • name: “”
  • scalefactor: 1.0
  • mean: Scalar()
Source

fn set_param( &mut self, layer: i32, num_param: i32, blob: &impl MatTraitConst, ) -> Result<()>

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.

Source

fn set_param_1( &mut self, layer_name: &str, num_param: i32, blob: &impl MatTraitConst, ) -> Result<()>

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

Enables or disables layer fusion in the network.

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

fn enable_winograd(&mut self, use_winograd: bool) -> Result<()>

Enables or disables the Winograd compute branch. The Winograd compute branch can speed up 3x3 Convolution at a small loss of accuracy.

§Parameters
  • useWinograd: true to enable the Winograd compute branch. The default is true.
Source

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. 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.

Dyn Compatibility§

This trait is not dyn compatible.

In older versions of Rust, dyn compatibility was called "object safety", so this trait is not object safe.

Implementors§