Yolo#

class model_api.models.yolo.DetectionBox(x, y, w, h)#

Bases: tuple

Create new instance of DetectionBox(x, y, w, h)

h#

Alias for field number 3

w#

Alias for field number 2

x#

Alias for field number 0

y#

Alias for field number 1

class model_api.models.yolo.YOLO(inference_adapter, configuration, preload=False)#

Bases: DetectionModel

Detection Model constructor

It extends the ImageModel construtor.

Parameters:

  • inference_adapter (InferenceAdapter) – allows working with the specified executor

  • configuration (dict, optional) – it contains values for parameters accepted by specific wrapper (confidence_threshold, labels etc.) which are set as data attributes

  • preload (bool, optional) – a flag whether the model is loaded to device while initialization. If preload=False, the model must be loaded via load method before inference

Raises:

WrapperError – if the model has more than 1 image inputs

class Params(param, sides)#

Bases: object

classmethod parameters()#

Defines the description and type of configurable data parameters for the wrapper.

See types.py to find available types of the data parameter. For each parameter the type, default value and description must be provided.

The example of possible data parameter:
‘confidence_threshold’: NumericalValue(

default_value=0.5, description=”Threshold value for detection box confidence”

)

The method must be implemented in each specific inherited wrapper.

Returns:

  • the dictionary with defined wrapper data parameters

postprocess(outputs, meta)#

Interface for postprocess method.

Parameters:

  • outputs (dict) –

    model raw output in the following format: {

    ’output_layer_name_1’: raw_result_1, ‘output_layer_name_2’: raw_result_2, …

    }

  • meta (dict) – the input metadata obtained from preprocess method

Returns:

  • postprocessed data in the format defined by wrapper

class model_api.models.yolo.YOLOF(inference_adapter, configuration={}, preload=False)#

Bases: YOLO

Detection Model constructor

It extends the ImageModel construtor.

Parameters:

  • inference_adapter (InferenceAdapter) – allows working with the specified executor

  • configuration (dict, optional) – it contains values for parameters accepted by specific wrapper (confidence_threshold, labels etc.) which are set as data attributes

  • preload (bool, optional) – a flag whether the model is loaded to device while initialization. If preload=False, the model must be loaded via load method before inference

Raises:

WrapperError – if the model has more than 1 image inputs

class Params(classes, num, sides, anchors)#

Bases: object

classmethod parameters()#

Defines the description and type of configurable data parameters for the wrapper.

See types.py to find available types of the data parameter. For each parameter the type, default value and description must be provided.

The example of possible data parameter:
‘confidence_threshold’: NumericalValue(

default_value=0.5, description=”Threshold value for detection box confidence”

)

The method must be implemented in each specific inherited wrapper.

Returns:

  • the dictionary with defined wrapper data parameters

class model_api.models.yolo.YOLOX(inference_adapter, configuration={}, preload=False)#

Bases: DetectionModel

Detection Model constructor

It extends the ImageModel construtor.

Parameters:

  • inference_adapter (InferenceAdapter) – allows working with the specified executor

  • configuration (dict, optional) – it contains values for parameters accepted by specific wrapper (confidence_threshold, labels etc.) which are set as data attributes

  • preload (bool, optional) – a flag whether the model is loaded to device while initialization. If preload=False, the model must be loaded via load method before inference

Raises:

WrapperError – if the model has more than 1 image inputs

classmethod parameters()#

Defines the description and type of configurable data parameters for the wrapper.

See types.py to find available types of the data parameter. For each parameter the type, default value and description must be provided.

The example of possible data parameter:
‘confidence_threshold’: NumericalValue(

default_value=0.5, description=”Threshold value for detection box confidence”

)

The method must be implemented in each specific inherited wrapper.

Returns:

  • the dictionary with defined wrapper data parameters

postprocess(outputs, meta)#

Interface for postprocess method.

Parameters:

  • outputs (dict) –

    model raw output in the following format: {

    ’output_layer_name_1’: raw_result_1, ‘output_layer_name_2’: raw_result_2, …

    }

  • meta (dict) – the input metadata obtained from preprocess method

Returns:

  • postprocessed data in the format defined by wrapper

preprocess(inputs)#

Data preprocess method

It performs basic preprocessing of a single image:

  • Resizes the image to fit the model input size via the defined resize type

  • Normalizes the image: subtracts means, divides by scales, switch channels BGR-RGB

  • Changes the image layout according to the model input layout

Also, it keeps the size of original image and resized one as original_shape and resized_shape in the metadata dictionary.

Note

It supports only models with single image input. If the model has more image inputs or has additional supported inputs, the preprocess should be overloaded in a specific wrapper.

Parameters:

inputs (ndarray) – a single image as 3D array in HWC layout

Returns:

{

‘input_layer_name’: preprocessed_image

}

  • the input metadata, which might be used in postprocess method

Return type:

  • the preprocessed image in the following format

set_strides_grids()#
class model_api.models.yolo.YOLOv5(inference_adapter, configuration, preload=False)#

Bases: DetectionModel

Reimplementation of ultralytics.YOLO

Detection Model constructor

It extends the ImageModel construtor.

Parameters:

  • inference_adapter (InferenceAdapter) – allows working with the specified executor

  • configuration (dict, optional) – it contains values for parameters accepted by specific wrapper (confidence_threshold, labels etc.) which are set as data attributes

  • preload (bool, optional) – a flag whether the model is loaded to device while initialization. If preload=False, the model must be loaded via load method before inference

Raises:

WrapperError – if the model has more than 1 image inputs

classmethod parameters()#

Defines the description and type of configurable data parameters for the wrapper.

See types.py to find available types of the data parameter. For each parameter the type, default value and description must be provided.

The example of possible data parameter:
‘confidence_threshold’: NumericalValue(

default_value=0.5, description=”Threshold value for detection box confidence”

)

The method must be implemented in each specific inherited wrapper.

Returns:

  • the dictionary with defined wrapper data parameters

postprocess(outputs, meta)#

Interface for postprocess method.

Parameters:

  • outputs (dict) –

    model raw output in the following format: {

    ’output_layer_name_1’: raw_result_1, ‘output_layer_name_2’: raw_result_2, …

    }

  • meta (dict) – the input metadata obtained from preprocess method

Returns:

  • postprocessed data in the format defined by wrapper

class model_api.models.yolo.YOLOv8(inference_adapter, configuration, preload=False)#

Bases: YOLOv5

YOLOv5 and YOLOv8 are identical in terms of inference

Detection Model constructor

It extends the ImageModel construtor.

Parameters:

  • inference_adapter (InferenceAdapter) – allows working with the specified executor

  • configuration (dict, optional) – it contains values for parameters accepted by specific wrapper (confidence_threshold, labels etc.) which are set as data attributes

  • preload (bool, optional) – a flag whether the model is loaded to device while initialization. If preload=False, the model must be loaded via load method before inference

Raises:

WrapperError – if the model has more than 1 image inputs

class model_api.models.yolo.YoloV3ONNX(inference_adapter, configuration={}, preload=False)#

Bases: DetectionModel

Detection Model constructor

It extends the ImageModel construtor.

Parameters:

  • inference_adapter (InferenceAdapter) – allows working with the specified executor

  • configuration (dict, optional) – it contains values for parameters accepted by specific wrapper (confidence_threshold, labels etc.) which are set as data attributes

  • preload (bool, optional) – a flag whether the model is loaded to device while initialization. If preload=False, the model must be loaded via load method before inference

Raises:

WrapperError – if the model has more than 1 image inputs

classmethod parameters()#

Defines the description and type of configurable data parameters for the wrapper.

See types.py to find available types of the data parameter. For each parameter the type, default value and description must be provided.

The example of possible data parameter:
‘confidence_threshold’: NumericalValue(

default_value=0.5, description=”Threshold value for detection box confidence”

)

The method must be implemented in each specific inherited wrapper.

Returns:

  • the dictionary with defined wrapper data parameters

postprocess(outputs, meta)#

Interface for postprocess method.

Parameters:

  • outputs (dict) –

    model raw output in the following format: {

    ’output_layer_name_1’: raw_result_1, ‘output_layer_name_2’: raw_result_2, …

    }

  • meta (dict) – the input metadata obtained from preprocess method

Returns:

  • postprocessed data in the format defined by wrapper

preprocess(inputs)#

Data preprocess method

It performs basic preprocessing of a single image:

  • Resizes the image to fit the model input size via the defined resize type

  • Normalizes the image: subtracts means, divides by scales, switch channels BGR-RGB

  • Changes the image layout according to the model input layout

Also, it keeps the size of original image and resized one as original_shape and resized_shape in the metadata dictionary.

Note

It supports only models with single image input. If the model has more image inputs or has additional supported inputs, the preprocess should be overloaded in a specific wrapper.

Parameters:

inputs (ndarray) – a single image as 3D array in HWC layout

Returns:

{

‘input_layer_name’: preprocessed_image

}

  • the input metadata, which might be used in postprocess method

Return type:

  • the preprocessed image in the following format

class model_api.models.yolo.YoloV4(inference_adapter, configuration={}, preload=False)#

Bases: YOLO

Detection Model constructor

It extends the ImageModel construtor.

Parameters:

  • inference_adapter (InferenceAdapter) – allows working with the specified executor

  • configuration (dict, optional) – it contains values for parameters accepted by specific wrapper (confidence_threshold, labels etc.) which are set as data attributes

  • preload (bool, optional) – a flag whether the model is loaded to device while initialization. If preload=False, the model must be loaded via load method before inference

Raises:

WrapperError – if the model has more than 1 image inputs

class Params(classes, num, sides, anchors, mask, layout)#

Bases: object

classmethod parameters()#

Defines the description and type of configurable data parameters for the wrapper.

See types.py to find available types of the data parameter. For each parameter the type, default value and description must be provided.

The example of possible data parameter:
‘confidence_threshold’: NumericalValue(

default_value=0.5, description=”Threshold value for detection box confidence”

)

The method must be implemented in each specific inherited wrapper.

Returns:

  • the dictionary with defined wrapper data parameters

model_api.models.yolo.permute_to_N_HWA_K(tensor, K, output_layout)#

Transpose/reshape a tensor from (N, (A x K), H, W) to (N, (HxWxA), K)

model_api.models.yolo.sigmoid(x)#
model_api.models.yolo.xywh2xyxy(xywh)#