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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
parent 31db8ed163
commit 5bce1c3021
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48 changed files with 418 additions and 420 deletions
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24
ultralytics/yolo/data/base.py
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@ -70,7 +70,7 @@ class BaseDataset(Dataset):
self.ni = len(self.labels)
# rect stuff
# Rect stuff
self.rect = rect
self.batch_size = batch_size
self.stride = stride
@ -79,13 +79,13 @@ class BaseDataset(Dataset):
assert self.batch_size is not None
self.set_rectangle()
# cache stuff
# Cache stuff
self.ims = [None] * self.ni
self.npy_files = [Path(f).with_suffix('.npy') for f in self.im_files]
if cache:
self.cache_images(cache)
# transforms
# Transforms
self.transforms = self.build_transforms(hyp=hyp)
def get_img_files(self, img_path):
@ -96,13 +96,13 @@ class BaseDataset(Dataset):
p = Path(p) # os-agnostic
if p.is_dir(): # dir
f += glob.glob(str(p / '**' / '*.*'), recursive=True)
# f = list(p.rglob('*.*')) # pathlib
# F = list(p.rglob('*.*')) # pathlib
elif p.is_file(): # file
with open(p) as t:
t = t.read().strip().splitlines()
parent = str(p.parent) + os.sep
f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path
# f += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib)
# F += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib)
else:
raise FileNotFoundError(f'{self.prefix}{p} does not exist')
im_files = sorted(x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS)
@ -113,7 +113,7 @@ class BaseDataset(Dataset):
return im_files
def update_labels(self, include_class: Optional[list]):
"""include_class, filter labels to include only these classes (optional)"""
"""include_class, filter labels to include only these classes (optional)."""
include_class_array = np.array(include_class).reshape(1, -1)
for i in range(len(self.labels)):
if include_class is not None:
@ -129,7 +129,7 @@ class BaseDataset(Dataset):
self.labels[i]['cls'][:, 0] = 0
def load_image(self, i):
# Loads 1 image from dataset index 'i', returns (im, resized hw)
"""Loads 1 image from dataset index 'i', returns (im, resized hw)."""
im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i]
if im is None: # not cached in RAM
if fn.exists(): # load npy
@ -147,7 +147,7 @@ class BaseDataset(Dataset):
return self.ims[i], self.im_hw0[i], self.im_hw[i] # im, hw_original, hw_resized
def cache_images(self, cache):
# cache images to memory or disk
"""Cache images to memory or disk."""
gb = 0 # Gigabytes of cached images
self.im_hw0, self.im_hw = [None] * self.ni, [None] * self.ni
fcn = self.cache_images_to_disk if cache == 'disk' else self.load_image
@ -164,7 +164,7 @@ class BaseDataset(Dataset):
pbar.close()
def cache_images_to_disk(self, i):
# Saves an image as an *.npy file for faster loading
"""Saves an image as an *.npy file for faster loading."""
f = self.npy_files[i]
if not f.exists():
np.save(f.as_posix(), cv2.imread(self.im_files[i]))
@ -211,17 +211,17 @@ class BaseDataset(Dataset):
return len(self.labels)
def update_labels_info(self, label):
"""custom your label format here"""
"""custom your label format here."""
return label
def build_transforms(self, hyp=None):
"""Users can custom augmentations here
like:
if self.augment:
# training transforms
# Training transforms
return Compose([])
else:
# val transforms
# Val transforms
return Compose([])
"""
raise NotImplementedError