# Copyright (c) OpenMMLab. All rights reserved. import os import os.path as osp import warnings from math import inf import torch.distributed as dist from torch.nn.modules.batchnorm import _BatchNorm from torch.utils.data import DataLoader try: from mmcv.runner import DistEvalHook as BasicDistEvalHook from mmcv.runner import EvalHook as BasicEvalHook from_mmcv = True class EvalHook(BasicEvalHook): greater_keys = [ 'acc', 'top', 'AR@', 'auc', 'precision', 'mAP@', 'Recall@' ] less_keys = ['loss'] def __init__(self, *args, save_best='auto', **kwargs): super().__init__(*args, save_best=save_best, **kwargs) class DistEvalHook(BasicDistEvalHook): greater_keys = [ 'acc', 'top', 'AR@', 'auc', 'precision', 'mAP@', 'Recall@' ] less_keys = ['loss'] def __init__(self, *args, save_best='auto', **kwargs): super().__init__(*args, save_best=save_best, **kwargs) except (ImportError, ModuleNotFoundError): warnings.warn('DeprecationWarning: EvalHook and DistEvalHook in mmaction2 ' 'will be deprecated, please install mmcv through master ' 'branch.') from_mmcv = False if not from_mmcv: from mmcv.runner import Hook class EvalHook(Hook): # noqa: F811 """Non-Distributed evaluation hook. Notes: If new arguments are added for EvalHook, tools/test.py, tools/eval_metric.py may be effected. This hook will regularly perform evaluation in a given interval when performing in non-distributed environment. Args: dataloader (DataLoader): A PyTorch dataloader. start (int | None, 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. Default: 1. by_epoch (bool): Determine perform evaluation by epoch or by iteration. If set to True, it will perform by epoch. Otherwise, by iteration. default: True. save_best (str | None, 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., ``top1_acc``, ``top5_acc``, ``mean_class_accuracy``, ``mean_average_precision``, ``mmit_mean_average_precision`` for action recognition dataset (RawframeDataset and VideoDataset). ``AR@AN``, ``auc`` for action localization dataset. (ActivityNetDataset). ``mAP@0.5IOU`` for spatio-temporal action detection dataset (AVADataset). If ``save_best`` is ``auto``, the first key of the returned ``OrderedDict`` result will be used. Default: 'auto'. rule (str | None, optional): Comparison rule for best score. If set to None, it will infer a reasonable rule. Keys such as 'acc', 'top' .etc will be inferred by 'greater' rule. Keys contain 'loss' will be inferred by 'less' rule. Options are 'greater', 'less', None. 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 = [ 'acc', 'top', 'AR@', 'auc', 'precision', 'mAP@', 'Recall@' ] less_keys = ['loss'] def __init__(self, dataloader, start=None, interval=1, by_epoch=True, save_best='auto', rule=None, **eval_kwargs): if 'key_indicator' in eval_kwargs: raise RuntimeError( '"key_indicator" is deprecated, ' 'you need to use "save_best" instead. ' 'See https://github.com/open-mmlab/mmaction2/pull/395 ' 'for more info') if not isinstance(dataloader, DataLoader): raise TypeError(f'dataloader must be a pytorch DataLoader, ' f'but got {type(dataloader)}') if interval <= 0: raise ValueError( f'interval must be positive, but got {interval}') assert isinstance(by_epoch, bool) 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.start = start self.by_epoch = by_epoch assert isinstance(save_best, str) or save_best is None self.save_best = save_best self.eval_kwargs = eval_kwargs self.initial_flag = True if self.save_best is not None: self.best_ckpt_path = 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()) def before_train_iter(self, runner): """Evaluate the model only at the start of training by iteration.""" if self.by_epoch: return if not self.initial_flag: return if self.start is not None and runner.iter >= self.start: self.after_train_iter(runner) self.initial_flag = False def before_train_epoch(self, runner): """Evaluate the model only at the start of training by epoch.""" if not self.by_epoch: return if not self.initial_flag: return if self.start is not None and runner.epoch >= self.start: self.after_train_epoch(runner) self.initial_flag = False def after_train_iter(self, runner): """Called after every training iter to evaluate the results.""" if not self.by_epoch: self._do_evaluate(runner) def after_train_epoch(self, runner): """Called after every training epoch to evaluate the results.""" if self.by_epoch: self._do_evaluate(runner) def _do_evaluate(self, runner): """perform evaluation and save ckpt.""" if not self.evaluation_flag(runner): return from mmaction.apis import single_gpu_test results = single_gpu_test(runner.model, self.dataloader) key_score = self.evaluate(runner, results) if self.save_best: self._save_ckpt(runner, key_score) def evaluation_flag(self, runner): """Judge whether to perform_evaluation. Returns: bool: The flag indicating whether to perform evaluation. """ if self.by_epoch: current = runner.epoch check_time = self.every_n_epochs else: current = runner.iter check_time = self.every_n_iters if self.start is None: if not check_time(runner, self.interval): # No evaluation during the interval. return False elif (current + 1) < self.start: # No evaluation if start is larger than the current time. return False else: # Evaluation only at epochs/iters 3, 5, 7... # if start==3 and interval==2 if (current + 1 - self.start) % self.interval: return False return True def _save_ckpt(self, runner, key_score): if self.by_epoch: current = f'epoch_{runner.epoch + 1}' cur_type, cur_time = 'epoch', runner.epoch + 1 else: current = f'iter_{runner.iter + 1}' cur_type, cur_time = 'iter', runner.iter + 1 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 if self.best_ckpt_path and osp.isfile(self.best_ckpt_path): os.remove(self.best_ckpt_path) best_ckpt_name = f'best_{self.key_indicator}_{current}.pth' runner.save_checkpoint( runner.work_dir, best_ckpt_name, create_symlink=False) self.best_ckpt_path = osp.join(runner.work_dir, best_ckpt_name) runner.meta['hook_msgs']['best_ckpt'] = self.best_ckpt_path runner.logger.info( f'Now best checkpoint is saved as {best_ckpt_name}.') runner.logger.info( f'Best {self.key_indicator} is {best_score:0.4f} ' f'at {cur_time} {cur_type}.') def evaluate(self, runner, results): """Evaluate the results. Args: runner (:obj:`mmcv.Runner`): The underlined training runner. results (list): Output 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] return None class DistEvalHook(EvalHook): # noqa: F811 """Distributed evaluation hook. This hook will regularly perform evaluation in a given interval when performing in distributed environment. Args: dataloader (DataLoader): A PyTorch dataloader. start (int | None, 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. Default: 1. by_epoch (bool): Determine perform evaluation by epoch or by iteration. If set to True, it will perform by epoch. Otherwise, by iteration. default: True. save_best (str | None, 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., ``top1_acc``, ``top5_acc``, ``mean_class_accuracy``, ``mean_average_precision``, ``mmit_mean_average_precision`` for action recognition dataset (RawframeDataset and VideoDataset). ``AR@AN``, ``auc`` for action localization dataset (ActivityNetDataset). ``mAP@0.5IOU`` for spatio-temporal action detection dataset (AVADataset). If ``save_best`` is ``auto``, the first key of the returned ``OrderedDict`` result will be used. Default: 'auto'. rule (str | None, optional): Comparison rule for best score. If set to None, it will infer a reasonable rule. Keys such as 'acc', 'top' .etc will be inferred by 'greater' rule. Keys contain 'loss' will be inferred by 'less' rule. Options are 'greater', 'less', None. Default: None. 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. 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, save_best='auto', rule=None, broadcast_bn_buffer=True, tmpdir=None, gpu_collect=False, **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 _do_evaluate(self, runner): """perform evaluation and save ckpt.""" # 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 _, 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) if not self.evaluation_flag(runner): return from mmaction.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_ckpt(runner, key_score)