HongChuang/ultralytics-ascend
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Resolve warnings (#19073)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2025-02-05 21:27:38 +09:00 • committed by GitHub
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2 changed files with 2 additions and 8 deletions
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4
ultralytics/engine/exporter.py
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@ -1544,10 +1544,10 @@ class NMSModel(torch.nn.Module):
Performs inference with NMS post-processing. Supports Detect, Segment, OBB and Pose. Performs inference with NMS post-processing. Supports Detect, Segment, OBB and Pose.
Args: Args:
x (torch.tensor): The preprocessed tensor with shape (N, 3, H, W). x (torch.Tensor): The preprocessed tensor with shape (N, 3, H, W).
Returns: Returns:
out (torch.tensor): The post-processed results with shape (N, max_det, 4 + 2 + extra_shape). out (torch.Tensor): The post-processed results with shape (N, max_det, 4 + 2 + extra_shape).
""" """
from functools import partial from functools import partial

6
ultralytics/nn/tasks.py
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@ -484,12 +484,6 @@ class RTDETRDetectionModel(DetectionModel):
the training and inference processes. RTDETR is an object detection and tracking model that extends from the the training and inference processes. RTDETR is an object detection and tracking model that extends from the
DetectionModel base class. DetectionModel base class.
Attributes:
cfg (str): The configuration file path or preset string. Default is 'rtdetr-l.yaml'.
ch (int): Number of input channels. Default is 3 (RGB).
nc (int, optional): Number of classes for object detection. Default is None.
verbose (bool): Specifies if summary statistics are shown during initialization. Default is True.
Methods: Methods:
init_criterion: Initializes the criterion used for loss calculation. init_criterion: Initializes the criterion used for loss calculation.
loss: Computes and returns the loss during training. loss: Computes and returns the loss during training.