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# Copyright (c) OpenMMLab. All rights reserved.
import copy
import logging
import os.path as osp
import warnings
from abc import ABCMeta, abstractmethod
import torch
from torch.optim import Optimizer
import annotator.uniformer.mmcv as mmcv
from ..parallel import is_module_wrapper
from .checkpoint import load_checkpoint
from .dist_utils import get_dist_info
from .hooks import HOOKS, Hook
from .log_buffer import LogBuffer
from .priority import Priority, get_priority
from .utils import get_time_str
class BaseRunner(metaclass=ABCMeta):
"""The base class of Runner, a training helper for PyTorch.
All subclasses should implement the following APIs:
- ``run()``
- ``train()``
- ``val()``
- ``save_checkpoint()``
Args:
model (:obj:`torch.nn.Module`): The model to be run.
batch_processor (callable): A callable method that process a data
batch. The interface of this method should be
`batch_processor(model, data, train_mode) -> dict`
optimizer (dict or :obj:`torch.optim.Optimizer`): It can be either an
optimizer (in most cases) or a dict of optimizers (in models that
requires more than one optimizer, e.g., GAN).
work_dir (str, optional): The working directory to save checkpoints
and logs. Defaults to None.
logger (:obj:`logging.Logger`): Logger used during training.
Defaults to None. (The default value is just for backward
compatibility)
meta (dict | None): A dict records some import information such as
environment info and seed, which will be logged in logger hook.
Defaults to None.
max_epochs (int, optional): Total training epochs.
max_iters (int, optional): Total training iterations.
"""
def __init__(self,
model,
batch_processor=None,
optimizer=None,
work_dir=None,
logger=None,
meta=None,
max_iters=None,
max_epochs=None):
if batch_processor is not None:
if not callable(batch_processor):
raise TypeError('batch_processor must be callable, '
f'but got {type(batch_processor)}')
warnings.warn('batch_processor is deprecated, please implement '
'train_step() and val_step() in the model instead.')
# raise an error is `batch_processor` is not None and
# `model.train_step()` exists.
if is_module_wrapper(model):
_model = model.module
else:
_model = model
if hasattr(_model, 'train_step') or hasattr(_model, 'val_step'):
raise RuntimeError(
'batch_processor and model.train_step()/model.val_step() '
'cannot be both available.')
else:
assert hasattr(model, 'train_step')
# check the type of `optimizer`
if isinstance(optimizer, dict):
for name, optim in optimizer.items():
if not isinstance(optim, Optimizer):
raise TypeError(
f'optimizer must be a dict of torch.optim.Optimizers, '
f'but optimizer["{name}"] is a {type(optim)}')
elif not isinstance(optimizer, Optimizer) and optimizer is not None:
raise TypeError(
f'optimizer must be a torch.optim.Optimizer object '
f'or dict or None, but got {type(optimizer)}')
# check the type of `logger`
if not isinstance(logger, logging.Logger):
raise TypeError(f'logger must be a logging.Logger object, '
f'but got {type(logger)}')
# check the type of `meta`
if meta is not None and not isinstance(meta, dict):
raise TypeError(
f'meta must be a dict or None, but got {type(meta)}')
self.model = model
self.batch_processor = batch_processor
self.optimizer = optimizer
self.logger = logger
self.meta = meta
# create work_dir
if mmcv.is_str(work_dir):
self.work_dir = osp.abspath(work_dir)
mmcv.mkdir_or_exist(self.work_dir)
elif work_dir is None:
self.work_dir = None
else:
raise TypeError('"work_dir" must be a str or None')
# get model name from the model class
if hasattr(self.model, 'module'):
self._model_name = self.model.module.__class__.__name__
else:
self._model_name = self.model.__class__.__name__
self._rank, self._world_size = get_dist_info()
self.timestamp = get_time_str()
self.mode = None
self._hooks = []
self._epoch = 0
self._iter = 0
self._inner_iter = 0
if max_epochs is not None and max_iters is not None:
raise ValueError(
'Only one of `max_epochs` or `max_iters` can be set.')
self._max_epochs = max_epochs
self._max_iters = max_iters
# TODO: Redesign LogBuffer, it is not flexible and elegant enough
self.log_buffer = LogBuffer()
@property
def model_name(self):
"""str: Name of the model, usually the module class name."""
return self._model_name
@property
def rank(self):
"""int: Rank of current process. (distributed training)"""
return self._rank
@property
def world_size(self):
"""int: Number of processes participating in the job.
(distributed training)"""
return self._world_size
@property
def hooks(self):
"""list[:obj:`Hook`]: A list of registered hooks."""
return self._hooks
@property
def epoch(self):
"""int: Current epoch."""
return self._epoch
@property
def iter(self):
"""int: Current iteration."""
return self._iter
@property
def inner_iter(self):
"""int: Iteration in an epoch."""
return self._inner_iter
@property
def max_epochs(self):
"""int: Maximum training epochs."""
return self._max_epochs
@property
def max_iters(self):
"""int: Maximum training iterations."""
return self._max_iters
@abstractmethod
def train(self):
pass
@abstractmethod
def val(self):
pass
@abstractmethod
def run(self, data_loaders, workflow, **kwargs):
pass
@abstractmethod
def save_checkpoint(self,
out_dir,
filename_tmpl,
save_optimizer=True,
meta=None,
create_symlink=True):
pass
def current_lr(self):
"""Get current learning rates.
Returns:
list[float] | dict[str, list[float]]: Current learning rates of all
param groups. If the runner has a dict of optimizers, this
method will return a dict.
"""
if isinstance(self.optimizer, torch.optim.Optimizer):
lr = [group['lr'] for group in self.optimizer.param_groups]
elif isinstance(self.optimizer, dict):
lr = dict()
for name, optim in self.optimizer.items():
lr[name] = [group['lr'] for group in optim.param_groups]
else:
raise RuntimeError(
'lr is not applicable because optimizer does not exist.')
return lr
def current_momentum(self):
"""Get current momentums.
Returns:
list[float] | dict[str, list[float]]: Current momentums of all
param groups. If the runner has a dict of optimizers, this
method will return a dict.
"""
def _get_momentum(optimizer):
momentums = []
for group in optimizer.param_groups:
if 'momentum' in group.keys():
momentums.append(group['momentum'])
elif 'betas' in group.keys():
momentums.append(group['betas'][0])
else:
momentums.append(0)
return momentums
if self.optimizer is None:
raise RuntimeError(
'momentum is not applicable because optimizer does not exist.')
elif isinstance(self.optimizer, torch.optim.Optimizer):
momentums = _get_momentum(self.optimizer)
elif isinstance(self.optimizer, dict):
momentums = dict()
for name, optim in self.optimizer.items():
momentums[name] = _get_momentum(optim)
return momentums
def register_hook(self, hook, priority='NORMAL'):
"""Register a hook into the hook list.
The hook will be inserted into a priority queue, with the specified
priority (See :class:`Priority` for details of priorities).
For hooks with the same priority, they will be triggered in the same
order as they are registered.
Args:
hook (:obj:`Hook`): The hook to be registered.
priority (int or str or :obj:`Priority`): Hook priority.
Lower value means higher priority.
"""
assert isinstance(hook, Hook)
if hasattr(hook, 'priority'):
raise ValueError('"priority" is a reserved attribute for hooks')
priority = get_priority(priority)
hook.priority = priority
# insert the hook to a sorted list
inserted = False
for i in range(len(self._hooks) - 1, -1, -1):
if priority >= self._hooks[i].priority:
self._hooks.insert(i + 1, hook)
inserted = True
break
if not inserted:
self._hooks.insert(0, hook)
def register_hook_from_cfg(self, hook_cfg):
"""Register a hook from its cfg.
Args:
hook_cfg (dict): Hook config. It should have at least keys 'type'
and 'priority' indicating its type and priority.
Notes:
The specific hook class to register should not use 'type' and
'priority' arguments during initialization.
"""
hook_cfg = hook_cfg.copy()
priority = hook_cfg.pop('priority', 'NORMAL')
hook = mmcv.build_from_cfg(hook_cfg, HOOKS)
self.register_hook(hook, priority=priority)
def call_hook(self, fn_name):
"""Call all hooks.
Args:
fn_name (str): The function name in each hook to be called, such as
"before_train_epoch".
"""
for hook in self._hooks:
getattr(hook, fn_name)(self)
def get_hook_info(self):
# Get hooks info in each stage
stage_hook_map = {stage: [] for stage in Hook.stages}
for hook in self.hooks:
try:
priority = Priority(hook.priority).name
except ValueError:
priority = hook.priority
classname = hook.__class__.__name__
hook_info = f'({priority:<12}) {classname:<35}'
for trigger_stage in hook.get_triggered_stages():
stage_hook_map[trigger_stage].append(hook_info)
stage_hook_infos = []
for stage in Hook.stages:
hook_infos = stage_hook_map[stage]
if len(hook_infos) > 0:
info = f'{stage}:\n'
info += '\n'.join(hook_infos)
info += '\n -------------------- '
stage_hook_infos.append(info)
return '\n'.join(stage_hook_infos)
def load_checkpoint(self,
filename,
map_location='cpu',
strict=False,
revise_keys=[(r'^module.', '')]):
return load_checkpoint(
self.model,
filename,
map_location,
strict,
self.logger,
revise_keys=revise_keys)
def resume(self,
checkpoint,
resume_optimizer=True,
map_location='default'):
if map_location == 'default':
if torch.cuda.is_available():
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)
else:
checkpoint = self.load_checkpoint(
checkpoint, map_location=map_location)
self._epoch = checkpoint['meta']['epoch']
self._iter = checkpoint['meta']['iter']
if self.meta is None:
self.meta = {}
self.meta.setdefault('hook_msgs', {})
# load `last_ckpt`, `best_score`, `best_ckpt`, etc. for hook messages
self.meta['hook_msgs'].update(checkpoint['meta'].get('hook_msgs', {}))
# Re-calculate the number of iterations when resuming
# models with different number of GPUs
if 'config' in checkpoint['meta']:
config = mmcv.Config.fromstring(
checkpoint['meta']['config'], file_format='.py')
previous_gpu_ids = config.get('gpu_ids', None)
if previous_gpu_ids and len(previous_gpu_ids) > 0 and len(
previous_gpu_ids) != self.world_size:
self._iter = int(self._iter * len(previous_gpu_ids) /
self.world_size)
self.logger.info('the iteration number is changed due to '
'change of GPU number')
# resume meta information meta
self.meta = checkpoint['meta']
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('resumed epoch %d, iter %d', self.epoch, self.iter)
def register_lr_hook(self, lr_config):
if lr_config is None:
return
elif isinstance(lr_config, dict):
assert 'policy' in lr_config
policy_type = lr_config.pop('policy')
# If the type of policy is all in lower case, e.g., 'cyclic',
# then its first letter will be capitalized, e.g., to be 'Cyclic'.
# This is for the convenient usage of Lr updater.
# Since this is not applicable for `
# CosineAnnealingLrUpdater`,
# the string will not be changed if it contains capital letters.
if policy_type == policy_type.lower():
policy_type = policy_type.title()
hook_type = policy_type + 'LrUpdaterHook'
lr_config['type'] = hook_type
hook = mmcv.build_from_cfg(lr_config, HOOKS)
else:
hook = lr_config
self.register_hook(hook, priority='VERY_HIGH')
def register_momentum_hook(self, momentum_config):
if momentum_config is None:
return
if isinstance(momentum_config, dict):
assert 'policy' in momentum_config
policy_type = momentum_config.pop('policy')
# If the type of policy is all in lower case, e.g., 'cyclic',
# then its first letter will be capitalized, e.g., to be 'Cyclic'.
# This is for the convenient usage of momentum updater.
# Since this is not applicable for
# `CosineAnnealingMomentumUpdater`,
# the string will not be changed if it contains capital letters.
if policy_type == policy_type.lower():
policy_type = policy_type.title()
hook_type = policy_type + 'MomentumUpdaterHook'
momentum_config['type'] = hook_type
hook = mmcv.build_from_cfg(momentum_config, HOOKS)
else:
hook = momentum_config
self.register_hook(hook, priority='HIGH')
def register_optimizer_hook(self, optimizer_config):
if optimizer_config is None:
return
if isinstance(optimizer_config, dict):
optimizer_config.setdefault('type', 'OptimizerHook')
hook = mmcv.build_from_cfg(optimizer_config, HOOKS)
else:
hook = optimizer_config
self.register_hook(hook, priority='ABOVE_NORMAL')
def register_checkpoint_hook(self, checkpoint_config):
if checkpoint_config is None:
return
if isinstance(checkpoint_config, dict):
checkpoint_config.setdefault('type', 'CheckpointHook')
hook = mmcv.build_from_cfg(checkpoint_config, HOOKS)
else:
hook = checkpoint_config
self.register_hook(hook, priority='NORMAL')
def register_logger_hooks(self, log_config):
if log_config is None:
return
log_interval = log_config['interval']
for info in log_config['hooks']:
logger_hook = mmcv.build_from_cfg(
info, HOOKS, default_args=dict(interval=log_interval))
self.register_hook(logger_hook, priority='VERY_LOW')
def register_timer_hook(self, timer_config):
if timer_config is None:
return
if isinstance(timer_config, dict):
timer_config_ = copy.deepcopy(timer_config)
hook = mmcv.build_from_cfg(timer_config_, HOOKS)
else:
hook = timer_config
self.register_hook(hook, priority='LOW')
def register_custom_hooks(self, custom_config):
if custom_config is None:
return
if not isinstance(custom_config, list):
custom_config = [custom_config]
for item in custom_config:
if isinstance(item, dict):
self.register_hook_from_cfg(item)
else:
self.register_hook(item, priority='NORMAL')
def register_profiler_hook(self, profiler_config):
if profiler_config is None:
return
if isinstance(profiler_config, dict):
profiler_config.setdefault('type', 'ProfilerHook')
hook = mmcv.build_from_cfg(profiler_config, HOOKS)
else:
hook = profiler_config
self.register_hook(hook)
def register_training_hooks(self,
lr_config,
optimizer_config=None,
checkpoint_config=None,
log_config=None,
momentum_config=None,
timer_config=dict(type='IterTimerHook'),
custom_hooks_config=None):
"""Register default and custom hooks for training.
Default and custom 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.
"""
self.register_lr_hook(lr_config)
self.register_momentum_hook(momentum_config)
self.register_optimizer_hook(optimizer_config)
self.register_checkpoint_hook(checkpoint_config)
self.register_timer_hook(timer_config)
self.register_logger_hooks(log_config)
self.register_custom_hooks(custom_hooks_config)
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