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Fix conversion ops using `clone` and `copy` (#4438)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Laughing 1 year ago committed by GitHub
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commit 8f79ce45c1
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3 changed files with 29 additions and 48 deletions
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3
ultralytics/utils/downloads.py
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@ -13,7 +13,7 @@ import requests
import torch import torch
from tqdm import tqdm from tqdm import tqdm
from ultralytics.utils import LOGGER, checks, clean_url, emojis, is_online, url2file from ultralytics.utils import LOGGER, TQDM_BAR_FORMAT, checks, clean_url, emojis, is_online, url2file
GITHUB_ASSET_NAMES = [f'yolov8{k}{suffix}.pt' for k in 'nsmlx' for suffix in ('', '6', '-cls', '-seg', '-pose')] + \ GITHUB_ASSET_NAMES = [f'yolov8{k}{suffix}.pt' for k in 'nsmlx' for suffix in ('', '6', '-cls', '-seg', '-pose')] + \
[f'yolov5{k}u.pt' for k in 'nsmlx'] + \ [f'yolov5{k}u.pt' for k in 'nsmlx'] + \
@ -287,7 +287,6 @@ def safe_download(url,
if method == 'torch': if method == 'torch':
torch.hub.download_url_to_file(url, f, progress=progress) torch.hub.download_url_to_file(url, f, progress=progress)
else: else:
from ultralytics.utils import TQDM_BAR_FORMAT
with request.urlopen(url) as response, tqdm(total=int(response.getheader('Content-Length', 0)), with request.urlopen(url) as response, tqdm(total=int(response.getheader('Content-Length', 0)),
desc=desc, desc=desc,
disable=not progress, disable=not progress,

36
ultralytics/utils/instance.py
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@ -24,15 +24,15 @@ to_2tuple = _ntuple(2)
to_4tuple = _ntuple(4) to_4tuple = _ntuple(4)
# `xyxy` means left top and right bottom # `xyxy` means left top and right bottom
# `xywh` means center x, center y and width, height(yolo format) # `xywh` means center x, center y and width, height(YOLO format)
# `ltwh` means left top and width, height(coco format) # `ltwh` means left top and width, height(COCO format)
_formats = ['xyxy', 'xywh', 'ltwh'] _formats = ['xyxy', 'xywh', 'ltwh']
__all__ = 'Bboxes', # tuple or list __all__ = 'Bboxes', # tuple or list
class Bboxes: class Bboxes:
"""Now only numpy is supported.""" """Bounding Boxes class. Only numpy variables are supported."""
def __init__(self, bboxes, format='xyxy') -> None: def __init__(self, bboxes, format='xyxy') -> None:
assert format in _formats, f'Invalid bounding box format: {format}, format must be one of {_formats}' assert format in _formats, f'Invalid bounding box format: {format}, format must be one of {_formats}'
@ -43,40 +43,18 @@ class Bboxes:
self.format = format self.format = format
# self.normalized = normalized # self.normalized = normalized
# def convert(self, format):
# assert format in _formats
# if self.format == format:
# bboxes = self.bboxes
# elif self.format == "xyxy":
# if format == "xywh":
# bboxes = xyxy2xywh(self.bboxes)
# else:
# bboxes = xyxy2ltwh(self.bboxes)
# elif self.format == "xywh":
# if format == "xyxy":
# bboxes = xywh2xyxy(self.bboxes)
# else:
# bboxes = xywh2ltwh(self.bboxes)
# else:
# if format == "xyxy":
# bboxes = ltwh2xyxy(self.bboxes)
# else:
# bboxes = ltwh2xywh(self.bboxes)
#
# return Bboxes(bboxes, format)
def convert(self, format): def convert(self, format):
"""Converts bounding box format from one type to another.""" """Converts bounding box format from one type to another."""
assert format in _formats, f'Invalid bounding box format: {format}, format must be one of {_formats}' assert format in _formats, f'Invalid bounding box format: {format}, format must be one of {_formats}'
if self.format == format: if self.format == format:
return return
elif self.format == 'xyxy': elif self.format == 'xyxy':
bboxes = xyxy2xywh(self.bboxes) if format == 'xywh' else xyxy2ltwh(self.bboxes) func = xyxy2xywh if format == 'xywh' else xyxy2ltwh
elif self.format == 'xywh': elif self.format == 'xywh':
bboxes = xywh2xyxy(self.bboxes) if format == 'xyxy' else xywh2ltwh(self.bboxes) func = xywh2xyxy if format == 'xyxy' else xywh2ltwh
else: else:
bboxes = ltwh2xyxy(self.bboxes) if format == 'xyxy' else ltwh2xywh(self.bboxes) func = ltwh2xyxy if format == 'xyxy' else ltwh2xywh
self.bboxes = bboxes self.bboxes = func(self.bboxes)
self.format = format self.format = format
def areas(self): def areas(self):

38
ultralytics/utils/ops.py
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@ -344,7 +344,8 @@ def xyxy2xywh(x):
Returns: Returns:
y (np.ndarray | torch.Tensor): The bounding box coordinates in (x, y, width, height) format. y (np.ndarray | torch.Tensor): The bounding box coordinates in (x, y, width, height) format.
""" """
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) assert x.shape[-1] == 4, f'input shape last dimension expected 4 but input shape is {x.shape}'
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) # faster than clone/copy
y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center
y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center
y[..., 2] = x[..., 2] - x[..., 0] # width y[..., 2] = x[..., 2] - x[..., 0] # width
@ -362,7 +363,8 @@ def xywh2xyxy(x):
Returns: Returns:
y (np.ndarray | torch.Tensor): The bounding box coordinates in (x1, y1, x2, y2) format. y (np.ndarray | torch.Tensor): The bounding box coordinates in (x1, y1, x2, y2) format.
""" """
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) assert x.shape[-1] == 4, f'input shape last dimension expected 4 but input shape is {x.shape}'
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) # faster than clone/copy
dw = x[..., 2] / 2 # half-width dw = x[..., 2] / 2 # half-width
dh = x[..., 3] / 2 # half-height dh = x[..., 3] / 2 # half-height
y[..., 0] = x[..., 0] - dw # top left x y[..., 0] = x[..., 0] - dw # top left x
@ -386,7 +388,8 @@ def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
y (np.ndarray | torch.Tensor): The coordinates of the bounding box in the format [x1, y1, x2, y2] where y (np.ndarray | torch.Tensor): The coordinates of the bounding box in the format [x1, y1, x2, y2] where
x1,y1 is the top-left corner, x2,y2 is the bottom-right corner of the bounding box. x1,y1 is the top-left corner, x2,y2 is the bottom-right corner of the bounding box.
""" """
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) assert x.shape[-1] == 4, f'input shape last dimension expected 4 but input shape is {x.shape}'
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) # faster than clone/copy
y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x
y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y
y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw # bottom right x y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw # bottom right x
@ -410,7 +413,8 @@ def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
""" """
if clip: if clip:
clip_boxes(x, (h - eps, w - eps)) # warning: inplace clip clip_boxes(x, (h - eps, w - eps)) # warning: inplace clip
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) assert x.shape[-1] == 4, f'input shape last dimension expected 4 but input shape is {x.shape}'
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) # faster than clone/copy
y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center
y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h # y center y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h # y center
y[..., 2] = (x[..., 2] - x[..., 0]) / w # width y[..., 2] = (x[..., 2] - x[..., 0]) / w # width
@ -431,7 +435,7 @@ def xyn2xy(x, w=640, h=640, padw=0, padh=0):
Returns: Returns:
y (np.ndarray | torch.Tensor): The x and y coordinates of the top left corner of the bounding box y (np.ndarray | torch.Tensor): The x and y coordinates of the top left corner of the bounding box
""" """
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = w * x[..., 0] + padw # top left x y[..., 0] = w * x[..., 0] + padw # top left x
y[..., 1] = h * x[..., 1] + padh # top left y y[..., 1] = h * x[..., 1] + padh # top left y
return y return y
@ -446,9 +450,9 @@ def xywh2ltwh(x):
Returns: Returns:
y (np.ndarray | torch.Tensor): The bounding box coordinates in the xyltwh format y (np.ndarray | torch.Tensor): The bounding box coordinates in the xyltwh format
""" """
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x
y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y
return y return y
@ -461,9 +465,9 @@ def xyxy2ltwh(x):
Returns: Returns:
y (np.ndarray | torch.Tensor): The bounding box coordinates in the xyltwh format. y (np.ndarray | torch.Tensor): The bounding box coordinates in the xyltwh format.
""" """
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[:, 2] = x[:, 2] - x[:, 0] # width y[..., 2] = x[..., 2] - x[..., 0] # width
y[:, 3] = x[:, 3] - x[:, 1] # height y[..., 3] = x[..., 3] - x[..., 1] # height
return y return y
@ -474,9 +478,9 @@ def ltwh2xywh(x):
Args: Args:
x (torch.Tensor): the input tensor x (torch.Tensor): the input tensor
""" """
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[:, 0] = x[:, 0] + x[:, 2] / 2 # center x y[..., 0] = x[..., 0] + x[..., 2] / 2 # center x
y[:, 1] = x[:, 1] + x[:, 3] / 2 # center y y[..., 1] = x[..., 1] + x[..., 3] / 2 # center y
return y return y
@ -569,9 +573,9 @@ def ltwh2xyxy(x):
Returns: Returns:
y (np.ndarray | torch.Tensor): the xyxy coordinates of the bounding boxes. y (np.ndarray | torch.Tensor): the xyxy coordinates of the bounding boxes.
""" """
y = torch.empty_like(x) if isinstance(x, torch.Tensor) else np.empty_like(x) y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[:, 2] = x[:, 2] + x[:, 0] # width y[..., 2] = x[..., 2] + x[..., 0] # width
y[:, 3] = x[:, 3] + x[:, 1] # height y[..., 3] = x[..., 3] + x[..., 1] # height
return y return y

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