mmpose.core.evaluation.eval_hooks 源代码
# Copyright (c) OpenMMLab. All rights reserved.
import tempfile
import warnings
from mmcv.runner import DistEvalHook as _DistEvalHook
from mmcv.runner import EvalHook as _EvalHook
MMPOSE_GREATER_KEYS = [
'acc', 'ap', 'ar', 'pck', 'auc', '3dpck', 'p-3dpck', '3dauc', 'p-3dauc'
]
MMPOSE_LESS_KEYS = ['loss', 'epe', 'nme', 'mpjpe', 'p-mpjpe', 'n-mpjpe']
[文档]class EvalHook(_EvalHook):
def __init__(self,
dataloader,
start=None,
interval=1,
by_epoch=True,
save_best=None,
rule=None,
test_fn=None,
greater_keys=MMPOSE_GREATER_KEYS,
less_keys=MMPOSE_LESS_KEYS,
**eval_kwargs):
if test_fn is None:
from mmpose.apis import single_gpu_test
test_fn = single_gpu_test
# to be compatible with the config before v0.16.0
# 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="AP".', DeprecationWarning)
key_indicator = eval_kwargs.pop('key_indicator', 'AP')
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)
[文档] 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
[文档]class DistEvalHook(_DistEvalHook):
def __init__(self,
dataloader,
start=None,
interval=1,
by_epoch=True,
save_best=None,
rule=None,
test_fn=None,
greater_keys=MMPOSE_GREATER_KEYS,
less_keys=MMPOSE_LESS_KEYS,
broadcast_bn_buffer=True,
tmpdir=None,
gpu_collect=False,
**eval_kwargs):
if test_fn is None:
from mmpose.apis import multi_gpu_test
test_fn = multi_gpu_test
# to be compatible with the config before v0.16.0
# 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="AP".', DeprecationWarning)
key_indicator = eval_kwargs.pop('key_indicator', 'AP')
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)
[文档] 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