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Reference for ultralytics/models/nas/predict.py

Note

This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/nas/predict.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!


ultralytics.models.nas.predict.NASPredictor 

NASPredictor(cfg=DEFAULT_CFG, overrides=None, _callbacks=None)

Bases: BasePredictor

Ultralytics YOLO NAS Predictor for object detection.

This class extends the BasePredictor from Ultralytics engine and is responsible for post-processing the raw predictions generated by the YOLO NAS models. It applies operations like non-maximum suppression and scaling the bounding boxes to fit the original image dimensions.

Attributes:

Name Type Description
args Namespace

Namespace containing various configurations for post-processing.
Example 
from ultralytics import NAS

model = NAS('yolo_nas_s')
predictor = model.predictor
# Assumes that raw_preds, img, orig_imgs are available
results = predictor.postprocess(raw_preds, img, orig_imgs)
Note

Typically, this class is not instantiated directly. It is used internally within the NAS class.

Parameters:

Name Type Description Default
cfg str

Path to a configuration file. Defaults to DEFAULT_CFG.

 DEFAULT_CFG
overrides dict

Configuration overrides. Defaults to None.

 None
Source code in ultralytics/engine/predictor.py 
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
    """
    Initializes the BasePredictor class.

    Args:
        cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG.
        overrides (dict, optional): Configuration overrides. Defaults to None.
    """
    self.args = get_cfg(cfg, overrides)
    self.save_dir = get_save_dir(self.args)
    if self.args.conf is None:
        self.args.conf = 0.25  # default conf=0.25
    self.done_warmup = False
    if self.args.show:
        self.args.show = check_imshow(warn=True)

    # Usable if setup is done
    self.model = None
    self.data = self.args.data  # data_dict
    self.imgsz = None
    self.device = None
    self.dataset = None
    self.vid_writer = {}  # dict of {save_path: video_writer, ...}
    self.plotted_img = None
    self.source_type = None
    self.seen = 0
    self.windows = []
    self.batch = None
    self.results = None
    self.transforms = None
    self.callbacks = _callbacks or callbacks.get_default_callbacks()
    self.txt_path = None
    self._lock = threading.Lock()  # for automatic thread-safe inference
    callbacks.add_integration_callbacks(self)

postprocess 

postprocess(preds_in, img, orig_imgs)

Postprocess predictions and returns a list of Results objects.

Source code in ultralytics/models/nas/predict.py 
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def postprocess(self, preds_in, img, orig_imgs):
    """Postprocess predictions and returns a list of Results objects."""

    # Cat boxes and class scores
    boxes = ops.xyxy2xywh(preds_in[0][0])
    preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1)

    preds = ops.non_max_suppression(
        preds,
        self.args.conf,
        self.args.iou,
        agnostic=self.args.agnostic_nms,
        max_det=self.args.max_det,
        classes=self.args.classes,
    )

    if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
        orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)

    results = []
    for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]):
        pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
        results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
    return results





Created 2023-11-12, Updated 2024-07-21
Authors: glenn-jocher (6), Burhan-Q (1)