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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import tempfile | |
| import warnings | |
| from mmcv.runner import DistEvalHook as BaseDistEvalHook | |
| from mmcv.runner import EvalHook as BaseEvalHook | |
| MMHUMAN3D_GREATER_KEYS = ['3dpck', 'pa-3dpck', '3dauc', 'pa-3dauc'] | |
| MMHUMAN3D_LESS_KEYS = ['mpjpe', 'pa-mpjpe', 'pve'] | |
| class EvalHook(BaseEvalHook): | |
| def __init__(self, | |
| dataloader, | |
| start=None, | |
| interval=1, | |
| by_epoch=True, | |
| save_best=None, | |
| rule=None, | |
| test_fn=None, | |
| greater_keys=MMHUMAN3D_GREATER_KEYS, | |
| less_keys=MMHUMAN3D_LESS_KEYS, | |
| **eval_kwargs): | |
| if test_fn is None: | |
| from detrsmpl.apis import single_gpu_test | |
| test_fn = single_gpu_test | |
| # remove "gpu_collect" from eval_kwargs | |
| if 'gpu_collect' in eval_kwargs: | |
| warnings.warn( | |
| '"gpu_collect" will be deprecated in EvalHook.' | |
| 'Please remove it from the config.', DeprecationWarning) | |
| _ = eval_kwargs.pop('gpu_collect') | |
| # update "save_best" according to "key_indicator" and remove the | |
| # latter from eval_kwargs | |
| if 'key_indicator' in eval_kwargs or isinstance(save_best, bool): | |
| warnings.warn( | |
| '"key_indicator" will be deprecated in EvalHook.' | |
| 'Please use "save_best" to specify the metric key,' | |
| 'e.g., save_best="pa-mpjpe".', DeprecationWarning) | |
| key_indicator = eval_kwargs.pop('key_indicator', None) | |
| if save_best is True and key_indicator is None: | |
| raise ValueError('key_indicator should not be None, when ' | |
| 'save_best is set to True.') | |
| save_best = key_indicator | |
| super().__init__(dataloader, start, interval, by_epoch, save_best, | |
| rule, test_fn, greater_keys, less_keys, **eval_kwargs) | |
| def evaluate(self, runner, results): | |
| with tempfile.TemporaryDirectory() as tmp_dir: | |
| eval_res = self.dataloader.dataset.evaluate(results, | |
| res_folder=tmp_dir, | |
| 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': | |
| self._init_rule(self.rule, list(eval_res.keys())[0]) | |
| return eval_res[self.key_indicator] | |
| return None | |
| class DistEvalHook(BaseDistEvalHook): | |
| def __init__(self, | |
| dataloader, | |
| start=None, | |
| interval=1, | |
| by_epoch=True, | |
| save_best=None, | |
| rule=None, | |
| test_fn=None, | |
| greater_keys=MMHUMAN3D_GREATER_KEYS, | |
| less_keys=MMHUMAN3D_LESS_KEYS, | |
| broadcast_bn_buffer=True, | |
| tmpdir=None, | |
| gpu_collect=False, | |
| **eval_kwargs): | |
| if test_fn is None: | |
| from detrsmpl.apis import multi_gpu_test | |
| test_fn = multi_gpu_test | |
| # update "save_best" according to "key_indicator" and remove the | |
| # latter from eval_kwargs | |
| if 'key_indicator' in eval_kwargs or isinstance(save_best, bool): | |
| warnings.warn( | |
| '"key_indicator" will be deprecated in EvalHook.' | |
| 'Please use "save_best" to specify the metric key,' | |
| 'e.g., save_best="pa-mpjpe".', DeprecationWarning) | |
| key_indicator = eval_kwargs.pop('key_indicator', None) | |
| if save_best is True and key_indicator is None: | |
| raise ValueError('key_indicator should not be None, when ' | |
| 'save_best is set to True.') | |
| save_best = key_indicator | |
| super().__init__(dataloader, start, interval, by_epoch, save_best, | |
| rule, test_fn, greater_keys, less_keys, | |
| broadcast_bn_buffer, tmpdir, gpu_collect, | |
| **eval_kwargs) | |
| def evaluate(self, runner, results): | |
| """Evaluate the results. | |
| Args: | |
| runner (:obj:`mmcv.Runner`): The underlined training runner. | |
| results (list): Output results. | |
| """ | |
| with tempfile.TemporaryDirectory() as tmp_dir: | |
| eval_res = self.dataloader.dataset.evaluate(results, | |
| res_folder=tmp_dir, | |
| 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 | |