mindpose.engine

mindpose.engine.create_evaluator(annotation_file, name='topdown', metric='AP', config=None, dataset_config=None, **kwargs)

Create evaluator engine. Evaluator engine is used to provide metric performance based on the provided prediction result.

Parameters:

• annotation_file (str) – Path of the annotation file. It only supports COCO-format now.

• name (str) – Name of the evaluation method. Default: “topdown”

• metric (Union[str, List[str]]) – Supported metrics. Default: “AP”

• config (Optional[Dict[str, Any]]) – Evaluaton config. Default: None

• dataset_config (Optional[Dict[str, Any]]) – Dataset config. Since the evaluation method sometimes relies on the dataset info. Default: None

• **kwargs (Any) – Arguments which feed into the evaluator

Return type:

Evaluator

Returns:

Evaluator engine for evaluation

mindpose.engine.create_inferencer(net, name='topdown_heatmap', config=None, dataset_config=None, **kwargs)

Create inference engine. Inference engine is used to perform model inference on the entire dataset based on the given method name.

Parameters:

• net (EvalNet) – Network for evaluation

• name (str) – Name of the inference method. Default: “topdown_heatmap”

• config (Optional[Dict[str, Any]]) – Inference config. Default: None

• dataset_config (Optional[Dict[str, Any]]) – Dataset config. Since the inference method sometimes relies on the dataset info. Default: None

• **kwargs (Any) – Arguments which feed into the inferencer

Return type:

Inferencer

Returns:

Inference engine for inferencing

mindpose.engine.inferencer

class mindpose.engine.inferencer.BottomUpHeatMapAEInferencer(net, config=None, progress_bar=False, decoder=None)

Bases: Inferencer

Create an inference engine for bottom-up heatmap with associative embedding based method. It runs the inference on the entire dataset and outputs a list of records.

Parameters:

• net (EvalNet) – Network for evaluation

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

• progress_bar (bool) – Display the progress bar during inferencing. Default: False

• decoder (Optional[BottomUpHeatMapAEDecoder]) – Decoder cell. It is used for hflip TTA. Default: None

Inputs:

dataset: Dataset

Outputs:

records: List of inference records.

infer(dataset)

Running the inference on the dataset. And return a list of records. Normally, in order to be compatible with the evaluator engine, each record should contains the following keys:

Keys:

pred: The predicted coordindate, in shape [M, 3(x_coord, y_coord, score)]

box: The coor bounding boxes, each record contains (center_x, center_y, scale_x, scale_y, area, bounding box score)

image_path: The path of the image

bbox_id: Bounding box ID

Parameters:

dataset (Dataset) – Dataset for inferencing

Return type:

List[Dict[str, Any]]

Returns:

List of inference results

load_inference_cfg()

Loading the inference config, where the returned config must be a dictionary which stores the configuration of the engine, such as the using TTA, etc.

Return type:

Dict[str, Any]

Returns:

Inference configurations

class mindpose.engine.inferencer.Inferencer(net, config=None)

Bases: object

Create an inference engine. It runs the inference on the entire dataset and outputs a list of records.

Parameters:

• net (EvalNet) – Network for inference

• config (Optional[Dict[str, Any]]) – Method-specific configuration for inference. Default: None

Inputs:

dataset: Dataset for inferencing

Outputs:

records: List of inference records

Note

This is an abstract class, child class must implement load_inference_cfg method.

infer(dataset)

Running the inference on the dataset. And return a list of records. Normally, in order to be compatible with the evaluator engine, each record should contains the following keys:

Keys:

pred: The predicted coordindate, in shape [C, 3(x_coord, y_coord, score)]

box: The coor bounding boxes, each record contains (center_x, center_y, scale_x, scale_y, area, bounding box score)

image_path: The path of the image

bbox_id: Bounding box ID

Parameters:

dataset (Dataset) – Dataset for inferencing

Return type:

List[Dict[str, Any]]

Returns:

List of inference results

load_inference_cfg()

Loading the inference config, where the returned config must be a dictionary which stores the configuration of the engine, such as the using TTA, etc.

Return type:

Dict[str, Any]

Returns:

Inference configurations

class mindpose.engine.inferencer.TopDownHeatMapInferencer(net, config=None, progress_bar=False, decoder=None)

Bases: Inferencer

Create an inference engine for Topdown heatmap based method. It runs the inference on the entire dataset and outputs a list of records.

Parameters:

• net (EvalNet) – Network for evaluation

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

• progress_bar (bool) – Display the progress bar during inferencing. Default: False

• decoder (Optional[TopDownHeatMapDecoder]) – Decoder cell. It is used for hflip TTA. Default: None

Inputs:

dataset: Dataset

Outputs:

records: List of inference records.

infer(dataset)

Running the inference on the dataset. And return a list of records. Normally, in order to be compatible with the evaluator engine, each record should contains the following keys:

Keys:

pred: The predicted coordindate, in shape [M, 3(x_coord, y_coord, score)]

box: The coor bounding boxes, each record contains (center_x, center_y, scale_x, scale_y, area, bounding box score)

image_path: The path of the image

bbox_id: Bounding box ID

Parameters:

dataset (Dataset) – Dataset for inferencing

Return type:

List[Dict[str, Any]]

Returns:

List of inference results

load_inference_cfg()

Loading the inference config, where the returned config must be a dictionary which stores the configuration of the engine, such as the using TTA, etc.

Return type:

Dict[str, Any]

Returns:

Inference configurations

mindpose.engine.evaluator

class mindpose.engine.evaluator.BottomUpEvaluator(annotation_file, metric='AP', num_joints=17, config=None, remove_result_file=True, result_path='./result_keypoints.json')

Bases: Evaluator

Create an evaluator based on BottomUp method. It performs the model evaluation based on the inference result (a list of records), and outputs with the metirc result.

Parameters:

• annotation_file (str) – Path of the annotation file. It only supports COCO-format.

• metric (Union[str, List[str]]) – Supported metrics. Default: “AP”

• num_joints (int) – Number of joints. Default: 17

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

• remove_result_file (bool) – Remove the cached result file after evaluation. Default: True

• result_path (str) – Path of the result file. Default: “./result_keypoints.json”

Inputs:

inference_result: Inference result from inference engine

Outputs:

evaluation_result: Evaluation result based on the metric

eval(inference_result)

Running the evaluation base on the inference result. Output the metric result.

Parameters:

inference_result (Dict[str, Any]) – List of inference records

Return type:

Dict[str, Any]

Returns:

metric result. Such as AP.5, etc.

load_evaluation_cfg()

Loading the evaluation config, where the returned config must be a dictionary which stores the configuration of the engine, such as the using soft-nms, etc.

Return type:

Dict[str, Any]

Returns:

Evaluation configurations

class mindpose.engine.evaluator.Evaluator(annotation_file, metric='AP', num_joints=17, config=None)

Bases: object

Create an evaluator engine. It performs the model evaluation based on the inference result (a list of records), and outputs with the metirc result.

Parameters:

• annotation_file (str) – Path of the annotation file. It only supports COCO-format now.

• metric (Union[str, List[str]]) – Supported metrics. Default: “AP”

• num_joints (int) – Number of joints. Default: 17

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

Inputs:

inference_result: Inference result from inference engine

Outputs:

evaluation_result: Evaluation result based on the metric

Note

This is an abstract class, child class must implement load_evaluation_cfg method.

eval(inference_result)

Running the evaluation base on the inference result. Output the metric result.

Parameters:

inference_result (Dict[str, Any]) – List of inference records

Return type:

Dict[str, Any]

Returns:

metric result. Such as AP.5, etc.

load_evaluation_cfg()

Loading the evaluation config, where the returned config must be a dictionary which stores the configuration of the engine, such as the using soft-nms, etc.

Return type:

Dict[str, Any]

Returns:

Evaluation configurations

property metrics: Set[str]

Returns the metrics used in evaluation.

class mindpose.engine.evaluator.TopDownEvaluator(annotation_file, metric='AP', num_joints=17, config=None, remove_result_file=True, result_path='./result_keypoints.json')

Bases: Evaluator

Create an evaluator based on Topdown method. It performs the model evaluation based on the inference result (a list of records), and outputs with the metirc result.

Parameters:

• annotation_file (str) – Path of the annotation file. It only supports COCO-format.

• metric (Union[str, List[str]]) – Supported metrics. Default: “AP”

• num_joints (int) – Number of joints. Default: 17

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

• remove_result_file (bool) – Remove the cached result file after evaluation. Default: True

• result_path (str) – Path of the result file. Default: “./result_keypoints.json”

Inputs:

inference_result: Inference result from inference engine

Outputs:

evaluation_result: Evaluation result based on the metric

eval(inference_result)

Running the evaluation base on the inference result. Output the metric result.

Parameters:

inference_result (Dict[str, Any]) – List of inference records

Return type:

Dict[str, Any]

Returns:

metric result. Such as AP.5, etc.

load_evaluation_cfg()

Loading the evaluation config, where the returned config must be a dictionary which stores the configuration of the engine, such as the using soft-nms, etc.

Return type:

Dict[str, Any]

Returns:

Evaluation configurations