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

ultralytics 8.0.98 add Baidu RT-DETR models (#2527)

Browse Source 
Co-authored-by: Kalen Michael <kalenmike@gmail.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: Dowon <ks2515@naver.com>
This commit is contained in:
Glenn Jocher
2023-05-11 01:15:20 +02:00
committed by GitHub
parent d1107ca4cb
commit 229119c376
33 changed files with 2172 additions and 834 deletions
Download Patch File Download Diff File Expand all files Collapse all files

16
ultralytics/yolo/engine/predictor.py
View File

@ -115,9 +115,7 @@ class BasePredictor:
im (torch.Tensor | List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list.
"""
if not isinstance(im, torch.Tensor):
same_shapes = all(x.shape == im[0].shape for x in im)
auto = same_shapes and self.model.pt
im = np.stack([LetterBox(self.imgsz, auto=auto, stride=self.model.stride)(image=x) for x in im])
im = np.stack(self.pre_transform(im))
im = im[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, BHWC to BCHW, (n, 3, h, w)
im = np.ascontiguousarray(im) # contiguous
im = torch.from_numpy(im)
@ -127,6 +125,18 @@ class BasePredictor:
img /= 255 # 0 - 255 to 0.0 - 1.0
return img
def pre_transform(self, im):
"""Pre-tranform input image before inference.
Args:
im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list.
Return: A list of transformed imgs.
"""
same_shapes = all(x.shape == im[0].shape for x in im)
auto = same_shapes and self.model.pt
return [LetterBox(self.imgsz, auto=auto, stride=self.model.stride)(image=x) for x in im]
def write_results(self, idx, results, batch):
"""Write inference results to a file or directory."""
p, im, _ = batch

3
ultralytics/yolo/utils/downloads.py
View File

@ -18,7 +18,8 @@ from ultralytics.yolo.utils import LOGGER, checks, clean_url, emojis, is_online,
GITHUB_ASSET_NAMES = [f'yolov8{k}{suffix}.pt' for k in 'nsmlx' for suffix in ('', '6', '-cls', '-seg', '-pose')] + \
[f'yolov5{k}u.pt' for k in 'nsmlx'] + \
[f'yolov3{k}u.pt' for k in ('', '-spp', '-tiny')] + \
[f'sam_{k}.pt' for k in 'bl']
[f'sam_{k}.pt' for k in 'bl'] + \
[f'rtdetr-{k}.pt' for k in 'lx']
GITHUB_ASSET_STEMS = [Path(k).stem for k in GITHUB_ASSET_NAMES]

2
ultralytics/yolo/utils/files.py
View File

@ -49,7 +49,7 @@ def increment_path(path, exist_ok=False, sep='', mkdir=False):
# Method 1
for n in range(2, 9999):
p = f'{path}{sep}{str(n).zfill(4)}{suffix}' # increment path
p = f'{path}{sep}{n}{suffix}' # increment path
if not os.path.exists(p): #
break
path = Path(p)

17
ultralytics/yolo/utils/torch_utils.py
View File

@ -25,7 +25,7 @@ TORCHVISION_0_10 = check_version(torchvision.__version__, '0.10.0')
TORCH_1_9 = check_version(torch.__version__, '1.9.0')
TORCH_1_11 = check_version(torch.__version__, '1.11.0')
TORCH_1_12 = check_version(torch.__version__, '1.12.0')
TORCH_2_X = check_version(torch.__version__, minimum='2.0')
TORCH_2_0 = check_version(torch.__version__, minimum='2.0')
@contextmanager
@ -85,7 +85,7 @@ def select_device(device='', batch=0, newline=False, verbose=True):
p = torch.cuda.get_device_properties(i)
s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB
arg = 'cuda:0'
elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available() and TORCH_2_X:
elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available() and TORCH_2_0:
# Prefer MPS if available
s += 'MPS\n'
arg = 'mps'
@ -274,11 +274,14 @@ def init_seeds(seed=0, deterministic=False):
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # for Multi-GPU, exception safe
# torch.backends.cudnn.benchmark = True # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287
if deterministic and TORCH_1_12: # https://github.com/ultralytics/yolov5/pull/8213
torch.use_deterministic_algorithms(True)
torch.backends.cudnn.deterministic = True
os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'
os.environ['PYTHONHASHSEED'] = str(seed)
if deterministic: # https://github.com/ultralytics/yolov5/pull/8213
if TORCH_2_0:
torch.use_deterministic_algorithms(True)
torch.backends.cudnn.deterministic = True
os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'
os.environ['PYTHONHASHSEED'] = str(seed)
else:
LOGGER.warning('WARNING ⚠️ Upgrade to torch>=2.0.0 for deterministic training.')
class ModelEMA: