mmdet.core.evaluation.eval_hooks 源代码
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import torch.distributed as dist
from mmcv.runner import DistEvalHook as BaseDistEvalHook
from mmcv.runner import EvalHook as BaseEvalHook
from torch.nn.modules.batchnorm import _BatchNorm
[文档]class EvalHook(BaseEvalHook):
def _do_evaluate(self, runner):
"""perform evaluation and save ckpt."""
if not self._should_evaluate(runner):
return
from mmdet.apis import single_gpu_test
results = single_gpu_test(runner.model, self.dataloader, show=False)
runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
key_score = self.evaluate(runner, results)
if self.save_best:
self._save_ckpt(runner, key_score)
[文档]class DistEvalHook(BaseDistEvalHook):
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 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)
if not self._should_evaluate(runner):
return
tmpdir = self.tmpdir
if tmpdir is None:
tmpdir = osp.join(runner.work_dir, '.eval_hook')
from mmdet.apis import multi_gpu_test
results = multi_gpu_test(
runner.model,
self.dataloader,
tmpdir=tmpdir,
gpu_collect=self.gpu_collect)
if runner.rank == 0:
print('\n')
runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
key_score = self.evaluate(runner, results)
if self.save_best:
self._save_ckpt(runner, key_score)