I want to use mmcv for distributed training. However, I had some problems.
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import platform
import shutil
import time
import warnings
from typing import Callable, Dict, List, Optional, Tuple, Union, no_type_check
import torch
from torch.optim import Optimizer
from torch.utils.data import DataLoader
import mmcv
from .base_runner import BaseRunner
from .builder import RUNNERS
from .checkpoint import save_checkpoint
from .hooks import IterTimerHook
from .utils import get_host_info
class IterLoader:
def __init__(self, dataloader: DataLoader):
self._dataloader = dataloader
self.iter_loader = iter(self._dataloader)
self._epoch = 0
@property
def epoch(self) -> int:
return self._epoch
def __next__(self):
try:
data = next(self.iter_loader)
except StopIteration:
self._epoch += 1
if hasattr(self._dataloader.sampler, 'set_epoch'):
self._dataloader.sampler.set_epoch(self._epoch)
time.sleep(2) # Prevent possible deadlock during epoch transition
self.iter_loader = iter(self._dataloader)
data = next(self.iter_loader)
return data
def __len__(self):
return len(self._dataloader)
@RUNNERS.register_module()
class IterBasedRunner(BaseRunner):
"""Iteration-based Runner.
This runner train models iteration by iteration.
"""
def train(self, data_loader, **kwargs):
self.model.train()
self.mode = 'train'
self.data_loader = data_loader
self._epoch = data_loader.epoch
data_batch = next(data_loader)
self.data_batch = data_batch
self.call_hook('before_train_iter')
outputs = self.model.train_step(data_batch, self.optimizer, **kwargs)
if not isinstance(outputs, dict):
raise TypeError('model.train_step() must return a dict')
if 'log_vars' in outputs:
self.log_buffer.update(outputs['log_vars'], outputs['num_samples'])
self.outputs = outputs
self.call_hook('after_train_iter')
del self.data_batch
self._inner_iter += 1
self._iter += 1
@torch.no_grad()
def val(self, data_loader, **kwargs):
self.model.eval()
self.mode = 'val'
self.data_loader = data_loader
data_batch = next(data_loader)
self.data_batch = data_batch
self.call_hook('before_val_iter')
outputs = self.model.val_step(data_batch, **kwargs)
if not isinstance(outputs, dict):
raise TypeError('model.val_step() must return a dict')
if 'log_vars' in outputs:
self.log_buffer.update(outputs['log_vars'], outputs['num_samples'])
self.outputs = outputs
self.call_hook('after_val_iter')
del self.data_batch
self._inner_iter += 1
def run(self,
data_loaders: List[DataLoader],
workflow: List[Tuple[str, int]],
max_iters: Optional[int] = None,
**kwargs) -> None:
"""Start running.
Args:
data_loaders (list[:obj:`DataLoader`]): Dataloaders for training
and validation.
workflow (list[tuple]): A list of (phase, iters) to specify the
running order and iterations. E.g, [('train', 10000),
('val', 1000)] means running 10000 iterations for training and
1000 iterations for validation, iteratively.
"""
assert isinstance(data_loaders, list)
assert mmcv.is_list_of(workflow, tuple)
assert len(data_loaders) == len(workflow)
if max_iters is not None:
warnings.warn(
'setting max_iters in run is deprecated, '
'please set max_iters in runner_config', DeprecationWarning)
self._max_iters = max_iters
assert self._max_iters is not None, (
'max_iters must be specified during instantiation')
work_dir = self.work_dir if self.work_dir is not None else 'NONE'
self.logger.info('Start running, host: %s, work_dir: %s',
get_host_info(), work_dir)
self.logger.info('Hooks will be executed in the following order:\n%s',
self.get_hook_info())
self.logger.info('workflow: %s, max: %d iters', workflow,
self._max_iters)
self.call_hook('before_run')
iter_loaders = [IterLoader(x) for x in data_loaders]
self.call_hook('before_epoch')
while self.iter < self._max_iters:
for i, flow in enumerate(workflow):
self._inner_iter = 0
mode, iters = flow
if not isinstance(mode, str) or not hasattr(self, mode):
raise ValueError(
'runner has no method named "{}" to run a workflow'.
format(mode))
iter_runner = getattr(self, mode)
for _ in range(iters):
if mode == 'train' and self.iter >= self._max_iters:
break
iter_runner(iter_loaders[i], **kwargs)
time.sleep(1) # wait for some hooks like loggers to finish
self.call_hook('after_epoch')
self.call_hook('after_run')
@no_type_check
def resume(self,
checkpoint: str,
resume_optimizer: bool = True,
map_location: Union[str, Callable] = 'default') -> None:
"""Resume model from checkpoint.
Args:
checkpoint (str): Checkpoint to resume from.
resume_optimizer (bool, optional): Whether resume the optimizer(s)
if the checkpoint file includes optimizer(s). Default to True.
map_location (str, optional): Same as :func:`torch.load`.
Default to 'default'.
"""
if map_location == 'default':
device_id = torch.cuda.current_device()
checkpoint = self.load_checkpoint(
checkpoint,
map_location=lambda storage, loc: storage.cuda(device_id))
else:
checkpoint = self.load_checkpoint(
checkpoint, map_location=map_location)
self._epoch = checkpoint['meta']['epoch']
self._iter = checkpoint['meta']['iter']
self._inner_iter = checkpoint['meta']['iter']
if 'optimizer' in checkpoint and resume_optimizer:
if isinstance(self.optimizer, Optimizer):
self.optimizer.load_state_dict(checkpoint['optimizer'])
elif isinstance(self.optimizer, dict):
for k in self.optimizer.keys():
self.optimizer[k].load_state_dict(
checkpoint['optimizer'][k])
else:
raise TypeError(
'Optimizer should be dict or torch.optim.Optimizer '
f'but got {type(self.optimizer)}')
self.logger.info(f'resumed from epoch: {self.epoch}, iter {self.iter}')
def save_checkpoint( # type: ignore
self,
out_dir: str,
filename_tmpl: str = 'iter_{}.pth',
meta: Optional[Dict] = None,
save_optimizer: bool = True,
create_symlink: bool = True) -> None:
"""Save checkpoint to file.
Args:
out_dir (str): Directory to save checkpoint files.
filename_tmpl (str, optional): Checkpoint file template.
Defaults to 'iter_{}.pth'.
meta (dict, optional): Metadata to be saved in checkpoint.
Defaults to None.
save_optimizer (bool, optional): Whether save optimizer.
Defaults to True.
create_symlink (bool, optional): Whether create symlink to the
latest checkpoint file. Defaults to True.
"""
if meta is None:
meta = {}
elif not isinstance(meta, dict):
raise TypeError(
f'meta should be a dict or None, but got {type(meta)}')
if self.meta is not None:
meta.update(self.meta)
# Note: meta.update(self.meta) should be done before
# meta.update(epoch=self.epoch + 1, iter=self.iter) otherwise
# there will be problems with resumed checkpoints.
# More details in https://github.com/open-mmlab/mmcv/pull/1108
meta.update(epoch=self.epoch + 1, iter=self.iter)
filename = filename_tmpl.format(self.iter + 1)
filepath = osp.join(out_dir, filename)
optimizer = self.optimizer if save_optimizer else None
save_checkpoint(self.model, filepath, optimizer=optimizer, meta=meta)
# in some environments, `os.symlink` is not supported, you may need to
# set `create_symlink` to False
if create_symlink:
dst_file = osp.join(out_dir, 'latest.pth')
if platform.system() != 'Windows':
mmcv.symlink(filename, dst_file)
else:
shutil.copy(filepath, dst_file)
def register_training_hooks(self,
lr_config,
optimizer_config=None,
checkpoint_config=None,
log_config=None,
momentum_config=None,
custom_hooks_config=None):
"""Register default hooks for iter-based training.
Checkpoint hook, optimizer stepper hook and logger hooks will be set to
`by_epoch=False` by default.
Default hooks include:
+----------------------+-------------------------+
| Hooks | Priority |
+======================+=========================+
| LrUpdaterHook | VERY_HIGH (10) |
+----------------------+-------------------------+
| MomentumUpdaterHook | HIGH (30) |
+----------------------+-------------------------+
| OptimizerStepperHook | ABOVE_NORMAL (40) |
+----------------------+-------------------------+
| CheckpointSaverHook | NORMAL (50) |
+----------------------+-------------------------+
| IterTimerHook | LOW (70) |
+----------------------+-------------------------+
| LoggerHook(s) | VERY_LOW (90) |
+----------------------+-------------------------+
| CustomHook(s) | defaults to NORMAL (50) |
+----------------------+-------------------------+
If custom hooks have same priority with default hooks, custom hooks
will be triggered after default hooks.
"""
if checkpoint_config is not None:
checkpoint_config.setdefault('by_epoch', False) # type: ignore
if lr_config is not None:
lr_config.setdefault('by_epoch', False) # type: ignore
if log_config is not None:
for info in log_config['hooks']:
info.setdefault('by_epoch', False)
super().register_training_hooks(
lr_config=lr_config,
momentum_config=momentum_config,
optimizer_config=optimizer_config,
checkpoint_config=checkpoint_config,
log_config=log_config,
timer_config=IterTimerHook(),
custom_hooks_config=custom_hooks_config)
Everything goes well except iter_loaders = [IterLoader(x) for x in data_loaders].
It is very strange. The sub-processes of distributed training will continue to start new sub-processes until the GPU‘s memory is out. When I debug, I find iter(self._dataloader) causes this problem. I am confused how to solve this problem. Is this problem caused by iter, or may it be caused elsewhere？
