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Update .pre-commit-config.yaml (#1026)

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Glenn Jocher 2023-02-17 22:26:40 +01:00 • committed by GitHub
parent 9047d737f4
commit edd3ff1669
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76 changed files with 928 additions and 935 deletions
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52
ultralytics/yolo/data/base.py
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@ -31,7 +31,7 @@ class BaseDataset(Dataset):
cache=False,
augment=True,
hyp=None,
prefix="",
prefix='',
rect=False,
batch_size=None,
stride=32,
@ -63,7 +63,7 @@ class BaseDataset(Dataset):
# cache stuff
self.ims = [None] * self.ni
self.npy_files = [Path(f).with_suffix(".npy") for f in self.im_files]
self.npy_files = [Path(f).with_suffix('.npy') for f in self.im_files]
if cache:
self.cache_images(cache)
@ -77,21 +77,21 @@ class BaseDataset(Dataset):
for p in img_path if isinstance(img_path, list) else [img_path]:
p = Path(p) # os-agnostic
if p.is_dir(): # dir
f += glob.glob(str(p / "**" / "*.*"), recursive=True)
f += glob.glob(str(p / '**' / '*.*'), recursive=True)
# 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 += [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)
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)
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)
# self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS]) # pathlib
assert im_files, f"{self.prefix}No images found"
assert im_files, f'{self.prefix}No images found'
except Exception as e:
raise FileNotFoundError(f"{self.prefix}Error loading data from {img_path}\n{HELP_URL}") from e
raise FileNotFoundError(f'{self.prefix}Error loading data from {img_path}\n{HELP_URL}') from e
return im_files
def update_labels(self, include_class: Optional[list]):
@ -99,16 +99,16 @@ class BaseDataset(Dataset):
include_class_array = np.array(include_class).reshape(1, -1)
for i in range(len(self.labels)):
if include_class:
cls = self.labels[i]["cls"]
bboxes = self.labels[i]["bboxes"]
segments = self.labels[i]["segments"]
cls = self.labels[i]['cls']
bboxes = self.labels[i]['bboxes']
segments = self.labels[i]['segments']
j = (cls == include_class_array).any(1)
self.labels[i]["cls"] = cls[j]
self.labels[i]["bboxes"] = bboxes[j]
self.labels[i]['cls'] = cls[j]
self.labels[i]['bboxes'] = bboxes[j]
if segments:
self.labels[i]["segments"] = segments[j]
self.labels[i]['segments'] = segments[j]
if self.single_cls:
self.labels[i]["cls"][:, 0] = 0
self.labels[i]['cls'][:, 0] = 0
def load_image(self, i):
# Loads 1 image from dataset index 'i', returns (im, resized hw)
@ -119,7 +119,7 @@ class BaseDataset(Dataset):
else: # read image
im = cv2.imread(f) # BGR
if im is None:
raise FileNotFoundError(f"Image Not Found {f}")
raise FileNotFoundError(f'Image Not Found {f}')
h0, w0 = im.shape[:2] # orig hw
r = self.imgsz / max(h0, w0) # ratio
if r != 1: # if sizes are not equal
@ -132,17 +132,17 @@ class BaseDataset(Dataset):
# 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
fcn = self.cache_images_to_disk if cache == 'disk' else self.load_image
with ThreadPool(NUM_THREADS) as pool:
results = pool.imap(fcn, range(self.ni))
pbar = tqdm(enumerate(results), total=self.ni, bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0)
for i, x in pbar:
if cache == "disk":
if cache == 'disk':
gb += self.npy_files[i].stat().st_size
else: # 'ram'
self.ims[i], self.im_hw0[i], self.im_hw[i] = x # im, hw_orig, hw_resized = load_image(self, i)
gb += self.ims[i].nbytes
pbar.desc = f"{self.prefix}Caching images ({gb / 1E9:.1f}GB {cache})"
pbar.desc = f'{self.prefix}Caching images ({gb / 1E9:.1f}GB {cache})'
pbar.close()
def cache_images_to_disk(self, i):
@ -155,7 +155,7 @@ class BaseDataset(Dataset):
bi = np.floor(np.arange(self.ni) / self.batch_size).astype(int) # batch index
nb = bi[-1] + 1 # number of batches
s = np.array([x.pop("shape") for x in self.labels]) # hw
s = np.array([x.pop('shape') for x in self.labels]) # hw
ar = s[:, 0] / s[:, 1] # aspect ratio
irect = ar.argsort()
self.im_files = [self.im_files[i] for i in irect]
@ -180,14 +180,14 @@ class BaseDataset(Dataset):
def get_label_info(self, index):
label = self.labels[index].copy()
label.pop("shape", None) # shape is for rect, remove it
label["img"], label["ori_shape"], label["resized_shape"] = self.load_image(index)
label["ratio_pad"] = (
label["resized_shape"][0] / label["ori_shape"][0],
label["resized_shape"][1] / label["ori_shape"][1],
label.pop('shape', None) # shape is for rect, remove it
label['img'], label['ori_shape'], label['resized_shape'] = self.load_image(index)
label['ratio_pad'] = (
label['resized_shape'][0] / label['ori_shape'][0],
label['resized_shape'][1] / label['ori_shape'][1],
) # for evaluation
if self.rect:
label["rect_shape"] = self.batch_shapes[self.batch[index]]
label['rect_shape'] = self.batch_shapes[self.batch[index]]
label = self.update_labels_info(label)
return label