ultralytics 8.0.80 single-line docstring fixes (#2060)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
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31db8ed163
commit
5bce1c3021
48 changed files with 418 additions and 420 deletions
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@ -50,7 +50,7 @@ for orientation in ExifTags.TAGS.keys():
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def get_hash(paths):
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# Returns a single hash value of a list of paths (files or dirs)
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"""Returns a single hash value of a list of paths (files or dirs)."""
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size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes
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h = hashlib.sha256(str(size).encode()) # hash sizes
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h.update(''.join(paths).encode()) # hash paths
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@ -58,7 +58,7 @@ def get_hash(paths):
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def exif_size(img):
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# Returns exif-corrected PIL size
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"""Returns exif-corrected PIL size."""
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s = img.size # (width, height)
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with contextlib.suppress(Exception):
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rotation = dict(img._getexif().items())[orientation]
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@ -94,7 +94,7 @@ def exif_transpose(image):
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def seed_worker(worker_id):
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# Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader
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"""Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader."""
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worker_seed = torch.initial_seed() % 2 ** 32
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np.random.seed(worker_seed)
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random.seed(worker_seed)
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@ -156,7 +156,7 @@ def create_dataloader(path,
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class InfiniteDataLoader(dataloader.DataLoader):
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""" Dataloader that reuses workers
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"""Dataloader that reuses workers
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Uses same syntax as vanilla DataLoader
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"""
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@ -175,7 +175,7 @@ class InfiniteDataLoader(dataloader.DataLoader):
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class _RepeatSampler:
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""" Sampler that repeats forever
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"""Sampler that repeats forever
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Args:
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sampler (Dataset.sampler): The sampler to repeat.
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@ -192,7 +192,7 @@ class _RepeatSampler:
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class LoadScreenshots:
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# YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"`
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def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None):
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# source = [screen_number left top width height] (pixels)
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"""source = [screen_number left top width height] (pixels)."""
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check_requirements('mss')
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import mss
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@ -224,7 +224,7 @@ class LoadScreenshots:
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return self
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def __next__(self):
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# mss screen capture: get raw pixels from the screen as np array
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"""mss screen capture: get raw pixels from the screen as np array."""
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im0 = np.array(self.sct.grab(self.monitor))[:, :, :3] # [:, :, :3] BGRA to BGR
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s = f'screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: '
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@ -320,7 +320,7 @@ class LoadImages:
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return path, im, im0, self.cap, s
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def _new_video(self, path):
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# Create a new video capture object
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"""Create a new video capture object."""
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self.frame = 0
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self.cap = cv2.VideoCapture(path)
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self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride)
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@ -328,7 +328,7 @@ class LoadImages:
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# self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0) # disable https://github.com/ultralytics/yolov5/issues/8493
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def _cv2_rotate(self, im):
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# Rotate a cv2 video manually
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"""Rotate a cv2 video manually."""
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if self.orientation == 0:
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return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE)
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elif self.orientation == 180:
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@ -379,7 +379,7 @@ class LoadStreams:
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self.threads[i].start()
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LOGGER.info('') # newline
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# check for common shapes
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# Check for common shapes
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s = np.stack([letterbox(x, img_size, stride=stride, auto=auto)[0].shape for x in self.imgs])
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self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal
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self.auto = auto and self.rect
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@ -388,7 +388,7 @@ class LoadStreams:
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LOGGER.warning('WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.')
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def update(self, i, cap, stream):
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# Read stream `i` frames in daemon thread
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"""Read stream `i` frames in daemon thread."""
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n, f = 0, self.frames[i] # frame number, frame array
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while cap.isOpened() and n < f:
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n += 1
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@ -428,13 +428,13 @@ class LoadStreams:
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def img2label_paths(img_paths):
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# Define label paths as a function of image paths
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"""Define label paths as a function of image paths."""
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sa, sb = f'{os.sep}images{os.sep}', f'{os.sep}labels{os.sep}' # /images/, /labels/ substrings
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return [sb.join(x.rsplit(sa, 1)).rsplit('.', 1)[0] + '.txt' for x in img_paths]
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class LoadImagesAndLabels(Dataset):
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# YOLOv5 train_loader/val_loader, loads images and labels for training and validation
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"""YOLOv5 train_loader/val_loader, loads images and labels for training and validation."""
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cache_version = 0.6 # dataset labels *.cache version
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rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]
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@ -590,7 +590,7 @@ class LoadImagesAndLabels(Dataset):
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pbar.close()
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def check_cache_ram(self, safety_margin=0.1, prefix=''):
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# Check image caching requirements vs available memory
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"""Check image caching requirements vs available memory."""
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b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes
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n = min(self.n, 30) # extrapolate from 30 random images
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for _ in range(n):
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@ -648,12 +648,6 @@ class LoadImagesAndLabels(Dataset):
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def __len__(self):
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return len(self.im_files)
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# def __iter__(self):
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# self.count = -1
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# print('ran dataset iter')
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# #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
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# return self
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def __getitem__(self, index):
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index = self.indices[index] # linear, shuffled, or image_weights
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@ -729,7 +723,7 @@ class LoadImagesAndLabels(Dataset):
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return torch.from_numpy(img), labels_out, self.im_files[index], shapes
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def load_image(self, i):
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# Loads 1 image from dataset index 'i', returns (im, original hw, resized hw)
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"""Loads 1 image from dataset index 'i', returns (im, original hw, resized hw)."""
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im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i],
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if im is None: # not cached in RAM
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if fn.exists(): # load npy
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@ -746,13 +740,13 @@ class LoadImagesAndLabels(Dataset):
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return self.ims[i], self.im_hw0[i], self.im_hw[i] # im, hw_original, hw_resized
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def cache_images_to_disk(self, i):
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# Saves an image as an *.npy file for faster loading
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"""Saves an image as an *.npy file for faster loading."""
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f = self.npy_files[i]
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if not f.exists():
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np.save(f.as_posix(), cv2.imread(self.im_files[i]))
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def load_mosaic(self, index):
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# YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic
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"""YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic."""
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labels4, segments4 = [], []
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s = self.img_size
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yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y
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@ -762,7 +756,7 @@ class LoadImagesAndLabels(Dataset):
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# Load image
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img, _, (h, w) = self.load_image(index)
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# place img in img4
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# Place img in img4
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if i == 0: # top left
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img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
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x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image)
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@ -810,7 +804,7 @@ class LoadImagesAndLabels(Dataset):
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return img4, labels4
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def load_mosaic9(self, index):
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# YOLOv5 9-mosaic loader. Loads 1 image + 8 random images into a 9-image mosaic
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"""YOLOv5 9-mosaic loader. Loads 1 image + 8 random images into a 9-image mosaic."""
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labels9, segments9 = [], []
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s = self.img_size
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indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices
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@ -820,7 +814,7 @@ class LoadImagesAndLabels(Dataset):
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# Load image
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img, _, (h, w) = self.load_image(index)
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# place img in img9
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# Place img in img9
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if i == 0: # center
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img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
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h0, w0 = h, w
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@ -888,7 +882,7 @@ class LoadImagesAndLabels(Dataset):
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@staticmethod
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def collate_fn(batch):
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# YOLOv8 collate function, outputs dict
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"""YOLOv8 collate function, outputs dict."""
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im, label, path, shapes = zip(*batch) # transposed
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for i, lb in enumerate(label):
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lb[:, 0] = i # add target image index for build_targets()
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@ -904,7 +898,7 @@ class LoadImagesAndLabels(Dataset):
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@staticmethod
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def collate_fn_old(batch):
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# YOLOv5 original collate function
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"""YOLOv5 original collate function."""
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im, label, path, shapes = zip(*batch) # transposed
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for i, lb in enumerate(label):
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lb[:, 0] = i # add target image index for build_targets()
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@ -913,7 +907,7 @@ class LoadImagesAndLabels(Dataset):
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# Ancillary functions --------------------------------------------------------------------------------------------------
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def flatten_recursive(path=DATASETS_DIR / 'coco128'):
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# Flatten a recursive directory by bringing all files to top level
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"""Flatten a recursive directory by bringing all files to top level."""
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new_path = Path(f'{str(path)}_flat')
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if os.path.exists(new_path):
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shutil.rmtree(new_path) # delete output folder
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@ -930,11 +924,11 @@ def extract_boxes(path=DATASETS_DIR / 'coco128'): # from utils.dataloaders impo
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n = len(files) # number of files
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for im_file in tqdm(files, total=n):
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if im_file.suffix[1:] in IMG_FORMATS:
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# image
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# Image
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im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB
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h, w = im.shape[:2]
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# labels
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# Labels
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lb_file = Path(img2label_paths([str(im_file)])[0])
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if Path(lb_file).exists():
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with open(lb_file) as f:
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@ -947,7 +941,7 @@ def extract_boxes(path=DATASETS_DIR / 'coco128'): # from utils.dataloaders impo
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f.parent.mkdir(parents=True)
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b = x[1:] * [w, h, w, h] # box
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# b[2:] = b[2:].max() # rectangle to square
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# B[2:] = b[2:].max() # rectangle to square
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b[2:] = b[2:] * 1.2 + 3 # pad
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b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int)
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@ -957,7 +951,7 @@ def extract_boxes(path=DATASETS_DIR / 'coco128'): # from utils.dataloaders impo
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def autosplit(path=DATASETS_DIR / 'coco128/images', weights=(0.9, 0.1, 0.0), annotated_only=False):
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""" Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
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"""Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
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Usage: from utils.dataloaders import *; autosplit()
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Arguments
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path: Path to images directory
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@ -983,11 +977,11 @@ def autosplit(path=DATASETS_DIR / 'coco128/images', weights=(0.9, 0.1, 0.0), ann
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def verify_image_label(args):
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# Verify one image-label pair
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"""Verify one image-label pair."""
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im_file, lb_file, prefix = args
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nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, '', [] # number (missing, found, empty, corrupt), message, segments
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try:
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# verify images
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# Verify images
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im = Image.open(im_file)
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im.verify() # PIL verify
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shape = exif_size(im) # image size
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@ -1000,7 +994,7 @@ def verify_image_label(args):
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ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100)
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msg = f'{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved'
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# verify labels
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# Verify labels
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if os.path.isfile(lb_file):
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nf = 1 # label found
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with open(lb_file) as f:
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@ -1077,7 +1071,7 @@ def create_classification_dataloader(path,
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rank=-1,
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workers=8,
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shuffle=True):
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# Returns Dataloader object to be used with YOLOv5 Classifier
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"""Returns Dataloader object to be used with YOLOv5 Classifier."""
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with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP
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dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache)
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batch_size = min(batch_size, len(dataset))
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