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ultralytics 8.0.46 TFLite and Benchmarks updates (#1141)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher
2023-02-25 09:24:14 -08:00
committed by GitHub
parent 3765f4f6d9
commit a82ee2c779
11 changed files with 130 additions and 74 deletions
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36
ultralytics/yolo/data/dataset.py
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@ -16,10 +16,28 @@ from .utils import HELP_URL, LOCAL_RANK, get_hash, img2label_paths, verify_image
class YOLODataset(BaseDataset):
cache_version = '1.0.1' # dataset labels *.cache version, >= 1.0.0 for YOLOv8
rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]
"""YOLO Dataset.
"""
Dataset class for loading images object detection and/or segmentation labels in YOLO format.
Args:
img_path (str): image path.
prefix (str): prefix.
img_path (str): path to the folder containing images.
imgsz (int): image size (default: 640).
cache (bool): if True, a cache file of the labels is created to speed up future creation of dataset instances
(default: False).
augment (bool): if True, data augmentation is applied (default: True).
hyp (dict): hyperparameters to apply data augmentation (default: None).
prefix (str): prefix to print in log messages (default: '').
rect (bool): if True, rectangular training is used (default: False).
batch_size (int): size of batches (default: None).
stride (int): stride (default: 32).
pad (float): padding (default: 0.0).
single_cls (bool): if True, single class training is used (default: False).
use_segments (bool): if True, segmentation masks are used as labels (default: False).
use_keypoints (bool): if True, keypoints are used as labels (default: False).
names (list): class names (default: None).
Returns:
A PyTorch dataset object that can be used for training an object detection or segmentation model.
"""
def __init__(self,
@ -44,7 +62,12 @@ class YOLODataset(BaseDataset):
super().__init__(img_path, imgsz, cache, augment, hyp, prefix, rect, batch_size, stride, pad, single_cls)
def cache_labels(self, path=Path('./labels.cache')):
# Cache dataset labels, check images and read shapes
"""Cache dataset labels, check images and read shapes.
Args:
path (Path): path where to save the cache file (default: Path('./labels.cache')).
Returns:
(dict): labels.
"""
x = {'labels': []}
nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages
desc = f'{self.prefix}Scanning {path.parent / path.stem}...'
@ -119,9 +142,8 @@ class YOLODataset(BaseDataset):
self.im_files = [lb['im_file'] for lb in labels] # update im_files
# Check if the dataset is all boxes or all segments
len_cls = sum(len(lb['cls']) for lb in labels)
len_boxes = sum(len(lb['bboxes']) for lb in labels)
len_segments = sum(len(lb['segments']) for lb in labels)
lengths = ((len(lb['cls']), len(lb['bboxes']), len(lb['segments'])) for lb in labels)
len_cls, len_boxes, len_segments = (sum(x) for x in zip(*lengths))
if len_segments and len_boxes != len_segments:
LOGGER.warning(
f'WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = {len_segments}, '