xdobro23/YOLOv8-16bit
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

ultralytics 8.0.59 new MLFlow and feature updates (#1720)

Browse Source 
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: St. HeMeow <sheng.heyang@gmail.com>
Co-authored-by: Danny Kim <imbird0312@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Torge Kummerow <CySlider@users.noreply.github.com>
Co-authored-by: dankernel <dkdkernel@gmail.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Roshanlal <roshanlaladchitre103@gmail.com>
Co-authored-by: Lorenzo Mammana <lorenzo.mammana@orobix.com>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
This commit is contained in:
Glenn Jocher
2023-03-31 20:33:02 +02:00
committed by GitHub
parent ccb6419835
commit e7876e1ba9
29 changed files with 326 additions and 160 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
ultralytics/yolo/engine/model.py
View File

@ -230,8 +230,9 @@ class YOLO:
return self.predictor.predict_cli(source=source) if is_cli else self.predictor(source=source, stream=stream)
def track(self, source=None, stream=False, **kwargs):
from ultralytics.tracker import register_tracker
register_tracker(self)
if not hasattr(self.predictor, 'trackers'):
from ultralytics.tracker import register_tracker
register_tracker(self)
# ByteTrack-based method needs low confidence predictions as input
conf = kwargs.get('conf') or 0.1
kwargs['conf'] = conf

20
ultralytics/yolo/engine/predictor.py
View File

@ -3,16 +3,16 @@
Run prediction on images, videos, directories, globs, YouTube, webcam, streams, etc.
Usage - sources:
$ yolo mode=predict model=yolov8n.pt --source 0 # webcam
img.jpg # image
vid.mp4 # video
screen # screenshot
path/ # directory
list.txt # list of images
list.streams # list of streams
'path/*.jpg' # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
$ yolo mode=predict model=yolov8n.pt source=0 # webcam
img.jpg # image
vid.mp4 # video
screen # screenshot
path/ # directory
list.txt # list of images
list.streams # list of streams
'path/*.jpg' # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
Usage - formats:
$ yolo mode=predict model=yolov8n.pt # PyTorch

26
ultralytics/yolo/engine/results.py
View File

@ -14,6 +14,7 @@ import torchvision.transforms.functional as F
from ultralytics.yolo.utils import LOGGER, SimpleClass, ops
from ultralytics.yolo.utils.plotting import Annotator, colors
from ultralytics.yolo.utils.torch_utils import TORCHVISION_0_10
class Results(SimpleClass):
@ -129,7 +130,10 @@ class Results(SimpleClass):
if masks is not None:
im = torch.as_tensor(annotator.im, dtype=torch.float16, device=masks.data.device).permute(2, 0, 1).flip(0)
im = F.resize(im.contiguous(), masks.data.shape[1:]) / 255
if TORCHVISION_0_10:
im = F.resize(im.contiguous(), masks.data.shape[1:], antialias=True) / 255
else:
im = F.resize(im.contiguous(), masks.data.shape[1:]) / 255
annotator.masks(masks.data, colors=[colors(x, True) for x in boxes.cls], im_gpu=im)
if probs is not None:
@ -259,7 +263,8 @@ class Masks(SimpleClass):
orig_shape (tuple): Original image size, in the format (height, width).
Properties:
segments (list): A list of segments which includes x, y, w, h, label, confidence, and mask of each detection.
xy (list): A list of segments (pixels) which includes x, y segments of each detection.
xyn (list): A list of segments (normalized) which includes x, y segments of each detection.
Methods:
cpu(): Returns a copy of the masks tensor on CPU memory.
@ -272,13 +277,28 @@ class Masks(SimpleClass):
self.masks = masks # N, h, w
self.orig_shape = orig_shape
def segments(self):
# Segments-deprecated (normalized)
LOGGER.warning("WARNING ⚠️ 'Masks.segments' is deprecated. Use 'Masks.xyn' for segments (normalized) and "
"'Masks.xy' for segments (pixels) instead.")
return self.xyn
@property
@lru_cache(maxsize=1)
def segments(self):
def xyn(self):
# Segments (normalized)
return [
ops.scale_segments(self.masks.shape[1:], x, self.orig_shape, normalize=True)
for x in ops.masks2segments(self.masks)]
@property
@lru_cache(maxsize=1)
def xy(self):
# Segments (pixels)
return [
ops.scale_segments(self.masks.shape[1:], x, self.orig_shape, normalize=False)
for x in ops.masks2segments(self.masks)]
@property
def shape(self):
return self.masks.shape

1
ultralytics/yolo/engine/trainer.py
View File

@ -370,6 +370,7 @@ class BaseTrainer:
self.epoch_time = tnow - self.epoch_time_start
self.epoch_time_start = tnow
self.run_callbacks('on_fit_epoch_end')
torch.cuda.empty_cache() # clears GPU vRAM at end of epoch, can help with out of memory errors
# Early Stopping
if RANK != -1: # if DDP training