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

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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>
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Glenn Jocher 2023-05-11 01:15:20 +02:00 • committed by GitHub
parent d1107ca4cb
commit 229119c376
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33 changed files with 2172 additions and 834 deletions
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17
ultralytics/yolo/utils/torch_utils.py
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@ -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: