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"""🎠Instance segmentation model for real-time inference"""
from typing import Any, Dict
from peekingduck.pipeline.nodes.model.yolact_edgev1 import yolact_edge_model
from peekingduck.pipeline.nodes.abstract_node import AbstractNode
[docs]class Node(AbstractNode):
"""Initializes and uses YolactEdge to infer from an image frame
The YolactEdge node is capable of detecting objects from 80 categories.
The table of object categories can be found
:ref:`here <general-object-detection-ids>`.
Inputs:
|img_data|
Outputs:
|bboxes_data|
|bbox_labels_data|
|bbox_scores_data|
|masks_data|
Configs:
model_type (:obj:`str`): (:obj:`str`): **{"r101-fpn", "r50-fpn",
"mobilenetv2"}, default="r50-fpn"**. |br|
weights_parent_dir (:obj:`Optional[str]`): **default = null**. |br|
Change the parent directory where weights will be stored by
replacing ``null`` with an absolute path to the desired directory.
input_size (:obj:`int`): **default = 550**. |br|
Input image resolution of the YolactEdge model.
detect (:obj:`List[Union[int, string]]`): **default=[0]**. |br|
List of object class names or IDs to be detected. To detect all classes,
refer to the :ref:`tech note <general-object-detection-ids>`.
max_num_detections: (:obj:`int`): **default=100**. |br|
Maximum number of detections per image, for all classes.
iou_threshold (:obj:`float`): **[0, 1], default = 0.5**. |br|
Overlapping bounding boxes with Intersection over Union (IoU) above
the threshold will be discarded.
score_threshold (:obj:`float`): **[0, 1], default = 0.2**. |br|
Bounding boxes with confidence score (product of objectness score
and classification score) below the threshold will be discarded.
References:
YolactEdge: Real-time Instance Segmentation on the Edge
https://arxiv.org/abs/2012.12259
Inference code and model weights:
https://github.com/haotian-liu/yolact_edge
"""
def __init__(self, config: Dict[str, Any] = None, **kwargs: Any) -> None:
super().__init__(config, node_path=__name__, **kwargs)
self.model = yolact_edge_model.YolactEdgeModel(self.config)
def run(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Reads `img` from `inputs` and return the bboxes and masks of the detected
objects.
The classes of objects to be detected can be specified through the
`detect` configuration option.
Args:
inputs (Dict): Inputs dictionary with the key `img`.
Returns:
(Dict): Outputs dictionary with the keys `bboxes`, `bbox_labels`,
`bbox_scores` and `masks`.
"""
bboxes, labels, scores, masks = self.model.predict(inputs["img"])
outputs = {
"bboxes": bboxes,
"bbox_labels": labels,
"bbox_scores": scores,
"masks": masks,
}
return outputs