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"""🔲 High performance anchor-free YOLO object detection model."""
from typing import Any, Dict, List, Optional, Union
import numpy as np
from peekingduck.pipeline.nodes.abstract_node import AbstractNode
from peekingduck.pipeline.nodes.model.yoloxv1 import yolox_model
[docs]class Node(AbstractNode): # pylint: disable=too-few-public-methods
"""Initializes and uses YOLOX to infer from an image frame.
The YOLOX node is capable detecting objects from 80 categories. The table
of object categories can be found
:ref:`here <general-object-detection-ids>`. The ``"yolox-tiny"`` model is
used by default and can be changed to one of ``("yolox-tiny", "yolox-s",
"yolox-m", "yolox-l")``.
Inputs:
|img_data|
Outputs:
|bboxes_data|
|bbox_labels_data|
|bbox_scores_data|
Configs:
model_format (:obj:`str`): **{"pytorch", "tensorrt"},
default="pytorch"** |br|
Defines the weights format of the model.
model_type (:obj:`str`): **{"yolox-tiny", "yolox-s", "yolox-m",
"yolox-l"}, default="yolox-tiny"**. |br|
Defines the type of YOLOX model to be used.
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=416**. |br|
Input image resolution of the YOLOX model.
detect (:obj:`List[Union[int, str]]`): **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>`.
iou_threshold (:obj:`float`): **[0, 1], default = 0.45**. |br|
Overlapping bounding boxes with Intersection over Union (IoU) above
the threshold will be discarded.
score_threshold (:obj:`float`): **[0, 1], default = 0.25**. |br|
Bounding boxes with confidence score (product of objectness score
and classification score) below the threshold will be discarded.
agnostic_nms (:obj:`bool`): **default = True**. |br|
Flag to determine if class-agnostic NMS (``torchvision.ops.nms``)
or class-aware NMS (``torchvision.ops.batched_nms``) should be
used.
half (:obj:`bool`): **default = False**. |br|
Flag to determine if half-precision floating-point should be used
for inference.
fuse (:obj:`bool`): **default = False**. |br|
Flag to determine if the convolution and batch normalization layers
should be fused for inference.
References:
YOLOX: Exceeding YOLO Series in 2021:
https://arxiv.org/abs/2107.08430
Inference code and model weights:
https://github.com/Megvii-BaseDetection/YOLOX
"""
def __init__(self, config: Dict[str, Any] = None, **kwargs: Any) -> None:
super().__init__(config, node_path=__name__, **kwargs)
self.model = yolox_model.YOLOXModel(self.config)
def run(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Reads `img` from `inputs` and return the bboxes of the detect
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`,
and `bbox_scores`.
"""
bboxes, labels, scores = self.model.predict(inputs["img"])
bboxes = np.clip(bboxes, 0, 1)
outputs = {"bboxes": bboxes, "bbox_labels": labels, "bbox_scores": scores}
return outputs
def _get_config_types(self) -> Dict[str, Any]:
"""Returns dictionary mapping the node's config keys to respective types."""
return {
"agnostic_nms": bool,
"detect": List[Union[int, str]],
"fuse": bool,
"half": bool,
"input_size": int,
"iou_threshold": float,
"model_format": str,
"model_type": str,
"score_threshold": float,
"weights_parent_dir": Optional[str],
}