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https://github.com/THU-MIG/yolov10.git
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d0b3c9812b
Co-authored-by: Laughing-q <1185102784@qq.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Ayush Chaurasia <ayushchaurasia@Ayushs-MacBook-Pro.local> Co-authored-by: Ayush Chaurasia <ayush.chuararsia@gmail.com>
71 lines
3.2 KiB
Python
71 lines
3.2 KiB
Python
import os
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from contextlib import contextmanager
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import torch
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import torch.distributed as dist
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from torch.nn.parallel import DistributedDataParallel as DDP
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from ultralytics.yolo.utils import check_version
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LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
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RANK = int(os.getenv('RANK', -1))
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WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
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@contextmanager
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def torch_distributed_zero_first(local_rank: int):
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# Decorator to make all processes in distributed training wait for each local_master to do something
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if local_rank not in [-1, 0]:
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dist.barrier(device_ids=[local_rank])
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yield
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if local_rank == 0:
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dist.barrier(device_ids=[0])
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def DDP_model(model):
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# Model DDP creation with checks
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assert not check_version(torch.__version__, '1.12.0', pinned=True), \
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'torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. ' \
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'Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395'
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if check_version(torch.__version__, '1.11.0'):
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return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True)
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else:
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return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
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def select_device(device='', batch_size=0, newline=True):
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# device = None or 'cpu' or 0 or '0' or '0,1,2,3'
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# s = f'YOLOv5 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} '
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s = f'YOLOv5 🚀 torch-{torch.__version__} '
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device = str(device).strip().lower().replace('cuda:', '').replace('none', '') # to string, 'cuda:0' to '0'
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cpu = device == 'cpu'
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mps = device == 'mps' # Apple Metal Performance Shaders (MPS)
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if cpu or mps:
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os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False
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elif device: # non-cpu device requested
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os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable - must be before assert is_available()
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assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \
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f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)"
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if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available
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devices = device.split(',') if device else '0' # range(torch.cuda.device_count()) # i.e. 0,1,6,7
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n = len(devices) # device count
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if n > 1 and batch_size > 0: # check batch_size is divisible by device_count
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assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'
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space = ' ' * (len(s) + 1)
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for i, d in enumerate(devices):
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p = torch.cuda.get_device_properties(i)
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s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB
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arg = 'cuda:0'
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elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available(): # prefer MPS if available
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s += 'MPS\n'
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arg = 'mps'
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else: # revert to CPU
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s += 'CPU\n'
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arg = 'cpu'
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if not newline:
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s = s.rstrip()
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print(s)
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return torch.device(arg)
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