import os.path as osp
import warnings
from math import inf
import mmcv
import torch.distributed as dist
from mmcv.runner import Hook
from torch.nn.modules.batchnorm import _BatchNorm
from torch.utils.data import DataLoader
from mmdet.utils import get_root_logger
[docs]class EvalHook(Hook):
"""Evaluation hook.
Notes:
If new arguments are added for EvalHook, tools/test.py,
tools/analysis_tools/eval_metric.py may be effected.
Attributes:
dataloader (DataLoader): A PyTorch dataloader.
start (int, optional): Evaluation starting epoch. It enables evaluation
before the training starts if ``start`` <= the resuming epoch.
If None, whether to evaluate is merely decided by ``interval``.
Default: None.
interval (int): Evaluation interval (by epochs). Default: 1.
save_best (str, optional): If a metric is specified, it would measure
the best checkpoint during evaluation. The information about best
checkpoint would be save in best.json.
Options are the evaluation metrics to the test dataset. e.g.,
``bbox_mAP``, ``segm_mAP`` for bbox detection and instance
segmentation. ``AR@100`` for proposal recall. If ``save_best`` is
``auto``, the first key will be used. The interval of
``CheckpointHook`` should device EvalHook. Default: None.
rule (str, optional): Comparison rule for best score. If set to None,
it will infer a reasonable rule. Keys such as 'mAP' or 'AR' will
be inferred by 'greater' rule. Keys contain 'loss' will be inferred
by 'less' rule. Options are 'greater', 'less'. Default: None.
**eval_kwargs: Evaluation arguments fed into the evaluate function of
the dataset.
"""
rule_map = {'greater': lambda x, y: x > y, 'less': lambda x, y: x < y}
init_value_map = {'greater': -inf, 'less': inf}
greater_keys = ['mAP', 'AR']
less_keys = ['loss']
def __init__(self,
dataloader,
start=None,
interval=1,
by_epoch=True,
save_best=None,
rule=None,
**eval_kwargs):
if not isinstance(dataloader, DataLoader):
raise TypeError('dataloader must be a pytorch DataLoader, but got'
f' {type(dataloader)}')
if not interval > 0:
raise ValueError(f'interval must be positive, but got {interval}')
if start is not None and start < 0:
warnings.warn(
f'The evaluation start epoch {start} is smaller than 0, '
f'use 0 instead', UserWarning)
start = 0
self.dataloader = dataloader
self.interval = interval
self.by_epoch = by_epoch
self.start = start
assert isinstance(save_best, str) or save_best is None
self.save_best = save_best
self.eval_kwargs = eval_kwargs
self.initial_epoch_flag = True
self.logger = get_root_logger()
if self.save_best is not None:
self._init_rule(rule, self.save_best)
def _init_rule(self, rule, key_indicator):
"""Initialize rule, key_indicator, comparison_func, and best score.
Args:
rule (str | None): Comparison rule for best score.
key_indicator (str | None): Key indicator to determine the
comparison rule.
"""
if rule not in self.rule_map and rule is not None:
raise KeyError(f'rule must be greater, less or None, '
f'but got {rule}.')
if rule is None:
if key_indicator != 'auto':
if any(key in key_indicator for key in self.greater_keys):
rule = 'greater'
elif any(key in key_indicator for key in self.less_keys):
rule = 'less'
else:
raise ValueError(f'Cannot infer the rule for key '
f'{key_indicator}, thus a specific rule '
f'must be specified.')
self.rule = rule
self.key_indicator = key_indicator
if self.rule is not None:
self.compare_func = self.rule_map[self.rule]
def before_run(self, runner):
if self.save_best is not None:
if runner.meta is None:
warnings.warn('runner.meta is None. Creating a empty one.')
runner.meta = dict()
runner.meta.setdefault('hook_msgs', dict())
[docs] def before_train_epoch(self, runner):
"""Evaluate the model only at the start of training."""
if not self.initial_epoch_flag:
return
if self.start is not None and runner.epoch >= self.start:
self.after_train_epoch(runner)
self.initial_epoch_flag = False
[docs] def evaluation_flag(self, runner):
"""Judge whether to perform_evaluation after this epoch.
Returns:
bool: The flag indicating whether to perform evaluation.
"""
if self.start is None:
if not self.every_n_epochs(runner, self.interval):
# No evaluation during the interval epochs.
return False
elif (runner.epoch + 1) < self.start:
# No evaluation if start is larger than the current epoch.
return False
else:
# Evaluation only at epochs 3, 5, 7... if start==3 and interval==2
if (runner.epoch + 1 - self.start) % self.interval:
return False
return True
def after_train_epoch(self, runner):
if not self.by_epoch or not self.evaluation_flag(runner):
return
from mmdet.apis import single_gpu_test
results = single_gpu_test(runner.model, self.dataloader, show=False)
key_score = self.evaluate(runner, results)
if self.save_best:
self.save_best_checkpoint(runner, key_score)
def after_train_iter(self, runner):
if self.by_epoch or not self.every_n_iters(runner, self.interval):
return
from mmdet.apis import single_gpu_test
results = single_gpu_test(runner.model, self.dataloader, show=False)
key_score = self.evaluate(runner, results)
if self.save_best:
self.save_best_checkpoint(runner, key_score)
def save_best_checkpoint(self, runner, key_score):
best_score = runner.meta['hook_msgs'].get(
'best_score', self.init_value_map[self.rule])
if self.compare_func(key_score, best_score):
best_score = key_score
runner.meta['hook_msgs']['best_score'] = best_score
last_ckpt = runner.meta['hook_msgs']['last_ckpt']
runner.meta['hook_msgs']['best_ckpt'] = last_ckpt
mmcv.symlink(
last_ckpt,
osp.join(runner.work_dir, f'best_{self.key_indicator}.pth'))
time_stamp = runner.epoch + 1 if self.by_epoch else runner.iter + 1
self.logger.info(f'Now best checkpoint is epoch_{time_stamp}.pth.'
f'Best {self.key_indicator} is {best_score:0.4f}')
def evaluate(self, runner, results):
eval_res = self.dataloader.dataset.evaluate(
results, logger=runner.logger, **self.eval_kwargs)
for name, val in eval_res.items():
runner.log_buffer.output[name] = val
runner.log_buffer.ready = True
if self.save_best is not None:
if self.key_indicator == 'auto':
# infer from eval_results
self._init_rule(self.rule, list(eval_res.keys())[0])
return eval_res[self.key_indicator]
else:
return None
[docs]class DistEvalHook(EvalHook):
"""Distributed evaluation hook.
Notes:
If new arguments are added, tools/test.py may be effected.
Attributes:
dataloader (DataLoader): A PyTorch dataloader.
start (int, optional): Evaluation starting epoch. It enables evaluation
before the training starts if ``start`` <= the resuming epoch.
If None, whether to evaluate is merely decided by ``interval``.
Default: None.
interval (int): Evaluation interval (by epochs). Default: 1.
tmpdir (str | None): Temporary directory to save the results of all
processes. Default: None.
gpu_collect (bool): Whether to use gpu or cpu to collect results.
Default: False.
save_best (str, optional): If a metric is specified, it would measure
the best checkpoint during evaluation. The information about best
checkpoint would be save in best.json.
Options are the evaluation metrics to the test dataset. e.g.,
``bbox_mAP``, ``segm_mAP`` for bbox detection and instance
segmentation. ``AR@100`` for proposal recall. If ``save_best`` is
``auto``, the first key will be used. The interval of
``CheckpointHook`` should device EvalHook. Default: None.
rule (str | None): Comparison rule for best score. If set to None,
it will infer a reasonable rule. Default: 'None'.
broadcast_bn_buffer (bool): Whether to broadcast the
buffer(running_mean and running_var) of rank 0 to other rank
before evaluation. Default: True.
**eval_kwargs: Evaluation arguments fed into the evaluate function of
the dataset.
"""
def __init__(self,
dataloader,
start=None,
interval=1,
by_epoch=True,
tmpdir=None,
gpu_collect=False,
save_best=None,
rule=None,
broadcast_bn_buffer=True,
**eval_kwargs):
super().__init__(
dataloader,
start=start,
interval=interval,
by_epoch=by_epoch,
save_best=save_best,
rule=rule,
**eval_kwargs)
self.broadcast_bn_buffer = broadcast_bn_buffer
self.tmpdir = tmpdir
self.gpu_collect = gpu_collect
def _broadcast_bn_buffer(self, runner):
# Synchronization of BatchNorm's buffer (running_mean
# and running_var) is not supported in the DDP of pytorch,
# which may cause the inconsistent performance of models in
# different ranks, so we broadcast BatchNorm's buffers
# of rank 0 to other ranks to avoid this.
if self.broadcast_bn_buffer:
model = runner.model
for name, module in model.named_modules():
if isinstance(module,
_BatchNorm) and module.track_running_stats:
dist.broadcast(module.running_var, 0)
dist.broadcast(module.running_mean, 0)
def after_train_epoch(self, runner):
if not self.by_epoch or not self.evaluation_flag(runner):
return
if self.broadcast_bn_buffer:
self._broadcast_bn_buffer(runner)
from mmdet.apis import multi_gpu_test
tmpdir = self.tmpdir
if tmpdir is None:
tmpdir = osp.join(runner.work_dir, '.eval_hook')
results = multi_gpu_test(
runner.model,
self.dataloader,
tmpdir=tmpdir,
gpu_collect=self.gpu_collect)
if runner.rank == 0:
print('\n')
key_score = self.evaluate(runner, results)
if self.save_best:
self.save_best_checkpoint(runner, key_score)
def after_train_iter(self, runner):
if self.by_epoch or not self.every_n_iters(runner, self.interval):
return
if self.broadcast_bn_buffer:
self._broadcast_bn_buffer(runner)
from mmdet.apis import multi_gpu_test
tmpdir = self.tmpdir
if tmpdir is None:
tmpdir = osp.join(runner.work_dir, '.eval_hook')
results = multi_gpu_test(
runner.model,
self.dataloader,
tmpdir=tmpdir,
gpu_collect=self.gpu_collect)
if runner.rank == 0:
print('\n')
key_score = self.evaluate(runner, results)
if self.save_best:
self.save_best_checkpoint(runner, key_score)