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ultralytics 8.0.80 single-line docstring fixes (#2060)

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Glenn Jocher 2023-04-16 15:20:11 +02:00 • committed by GitHub
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10
ultralytics/nn/autoshape.py
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@ -24,7 +24,7 @@ from ultralytics.yolo.utils.torch_utils import copy_attr, smart_inference_mode
class AutoShape(nn.Module):
# YOLOv8 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
"""YOLOv8 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS."""
conf = 0.25 # NMS confidence threshold
iou = 0.45 # NMS IoU threshold
agnostic = False # NMS class-agnostic
@ -47,7 +47,7 @@ class AutoShape(nn.Module):
m.export = True # do not output loss values
def _apply(self, fn):
# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
"""Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers."""
self = super()._apply(fn)
if self.pt:
m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect()
@ -59,7 +59,7 @@ class AutoShape(nn.Module):
@smart_inference_mode()
def forward(self, ims, size=640, augment=False, profile=False):
# Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:
"""Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:."""
# file: ims = 'data/images/zidane.jpg' # str or PosixPath
# URI: = 'https://ultralytics.com/images/zidane.jpg'
# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)
@ -202,7 +202,7 @@ class Detections:
return self.ims
def pandas(self):
# return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
"""Return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])."""
import pandas
new = copy(self) # return copy
ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns
@ -213,7 +213,7 @@ class Detections:
return new
def tolist(self):
# return a list of Detections objects, i.e. 'for result in results.tolist():'
"""Return a list of Detections objects, i.e. 'for result in results.tolist():'."""
r = range(self.n) # iterable
x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
# for d in x: