dasha_f
6e1a22ba8b
assets/conveyors (274 МБ) - ленты и угловая секция NVIDIA, на которые ссылается сцена относительным путём. Раньше исключались как перекачиваемые, но без них сцена не композится из коробки. cv/ - код стереодвижков, которые вызывает control_test, без весов: * defom-stereo - рабочий бейзлайн (DEFOM vitl, вход 480, iters 24) * crestereo - второй движок, точнее по габаритам (MAE 23.5 против 32.8 мм) * fast-foundationstereo - проверялся, в бейзлайн не вошёл * circular_section.py - показатель кругового сечения, перенесён в measure_plane.py: выравнивает облако по СОБСТВЕННЫМ главным осям и режет на пяти высотах вдоль каждой. Три самодельные версии (мировые оси, одно сечение) давали хуже; результаты проверки на эталонной геометрии - в circular_section_results.json Веса по-прежнему не в репозитории - источники в MODELS.md. Наборы кадров прежних прогонов (cv/flow_*, 1.26 ГБ) исключены: это выход, а не исходники. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
106 lines
3.6 KiB
Python
106 lines
3.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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# https://github.com/open-mmlab/mmcv/blob/7540cf73ac7e5d1e14d0ffbd9b6759e83929ecfc/mmcv/runner/dist_utils.py
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import os
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import subprocess
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import torch
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import torch.multiprocessing as mp
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from torch import distributed as dist
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def init_dist(launcher, backend='nccl', **kwargs):
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if mp.get_start_method(allow_none=True) is None:
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mp.set_start_method('spawn')
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if launcher == 'pytorch':
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_init_dist_pytorch(backend, **kwargs)
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elif launcher == 'mpi':
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_init_dist_mpi(backend, **kwargs)
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elif launcher == 'slurm':
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_init_dist_slurm(backend, **kwargs)
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else:
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raise ValueError(f'Invalid launcher type: {launcher}')
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def _init_dist_pytorch(backend, **kwargs):
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# TODO: use local_rank instead of rank % num_gpus
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rank = int(os.environ['RANK'])
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num_gpus = torch.cuda.device_count()
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torch.cuda.set_device(rank % num_gpus)
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dist.init_process_group(backend=backend, **kwargs)
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def _init_dist_mpi(backend, **kwargs):
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# TODO: use local_rank instead of rank % num_gpus
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rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
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num_gpus = torch.cuda.device_count()
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torch.cuda.set_device(rank % num_gpus)
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dist.init_process_group(backend=backend, **kwargs)
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def _init_dist_slurm(backend, port=None):
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"""Initialize slurm distributed training environment.
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If argument ``port`` is not specified, then the master port will be system
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environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system
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environment variable, then a default port ``29500`` will be used.
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Args:
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backend (str): Backend of torch.distributed.
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port (int, optional): Master port. Defaults to None.
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"""
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proc_id = int(os.environ['SLURM_PROCID'])
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ntasks = int(os.environ['SLURM_NTASKS'])
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node_list = os.environ['SLURM_NODELIST']
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num_gpus = torch.cuda.device_count()
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torch.cuda.set_device(proc_id % num_gpus)
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addr = subprocess.getoutput(
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f'scontrol show hostname {node_list} | head -n1')
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# specify master port
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if port is not None:
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os.environ['MASTER_PORT'] = str(port)
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elif 'MASTER_PORT' in os.environ:
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pass # use MASTER_PORT in the environment variable
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else:
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# 29500 is torch.distributed default port
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os.environ['MASTER_PORT'] = '29500'
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# use MASTER_ADDR in the environment variable if it already exists
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if 'MASTER_ADDR' not in os.environ:
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os.environ['MASTER_ADDR'] = addr
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os.environ['WORLD_SIZE'] = str(ntasks)
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os.environ['LOCAL_RANK'] = str(proc_id % num_gpus)
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os.environ['RANK'] = str(proc_id)
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dist.init_process_group(backend=backend)
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def get_dist_info():
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# if (TORCH_VERSION != 'parrots'
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# and digit_version(TORCH_VERSION) < digit_version('1.0')):
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# initialized = dist._initialized
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# else:
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if dist.is_available():
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initialized = dist.is_initialized()
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else:
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initialized = False
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if initialized:
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rank = dist.get_rank()
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world_size = dist.get_world_size()
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else:
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rank = 0
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world_size = 1
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return rank, world_size
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# from DETR repo
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def setup_for_distributed(is_master):
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"""
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This function disables printing when not in master process
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"""
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import builtins as __builtin__
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builtin_print = __builtin__.print
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def print(*args, **kwargs):
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force = kwargs.pop('force', False)
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if is_master or force:
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builtin_print(*args, **kwargs)
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__builtin__.print = print
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