100 lines
3.6 KiB
Python
100 lines
3.6 KiB
Python
import os.path as osp
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import torch.distributed as dist
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from mmcv.runner import DistEvalHook as _DistEvalHook
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from mmcv.runner import EvalHook as _EvalHook
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from torch.nn.modules.batchnorm import _BatchNorm
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class EvalHook(_EvalHook):
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"""Single GPU EvalHook, with efficient test support.
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Args:
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by_epoch (bool): Determine perform evaluation by epoch or by iteration.
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If set to True, it will perform by epoch. Otherwise, by iteration.
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Default: False.
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efficient_test (bool): Whether save the results as local numpy files to
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save CPU memory during evaluation. Default: False.
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Returns:
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list: The prediction results.
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"""
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greater_keys = ['mIoU', 'mAcc', 'aAcc']
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def __init__(self, *args, by_epoch=False, efficient_test=False, **kwargs):
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super().__init__(*args, by_epoch=by_epoch, **kwargs)
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self.efficient_test = efficient_test
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def _do_evaluate(self, runner):
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"""perform evaluation and save ckpt."""
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if not self._should_evaluate(runner):
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return
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from mmseg.apis import single_gpu_test
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results = single_gpu_test(
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runner.model,
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self.dataloader,
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show=False,
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efficient_test=self.efficient_test)
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runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
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key_score = self.evaluate(runner, results)
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if self.save_best:
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self._save_ckpt(runner, key_score)
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class DistEvalHook(_DistEvalHook):
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"""Distributed EvalHook, with efficient test support.
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Args:
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by_epoch (bool): Determine perform evaluation by epoch or by iteration.
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If set to True, it will perform by epoch. Otherwise, by iteration.
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Default: False.
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efficient_test (bool): Whether save the results as local numpy files to
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save CPU memory during evaluation. Default: False.
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Returns:
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list: The prediction results.
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"""
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greater_keys = ['mIoU', 'mAcc', 'aAcc']
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def __init__(self, *args, by_epoch=False, efficient_test=False, **kwargs):
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super().__init__(*args, by_epoch=by_epoch, **kwargs)
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self.efficient_test = efficient_test
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def _do_evaluate(self, runner):
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"""perform evaluation and save ckpt."""
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# Synchronization of BatchNorm's buffer (running_mean
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# and running_var) is not supported in the DDP of pytorch,
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# which may cause the inconsistent performance of models in
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# different ranks, so we broadcast BatchNorm's buffers
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# of rank 0 to other ranks to avoid this.
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if self.broadcast_bn_buffer:
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model = runner.model
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for name, module in model.named_modules():
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if isinstance(module,
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_BatchNorm) and module.track_running_stats:
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dist.broadcast(module.running_var, 0)
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dist.broadcast(module.running_mean, 0)
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if not self._should_evaluate(runner):
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return
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tmpdir = self.tmpdir
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if tmpdir is None:
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tmpdir = osp.join(runner.work_dir, '.eval_hook')
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from mmseg.apis import multi_gpu_test
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results = multi_gpu_test(
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runner.model,
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self.dataloader,
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tmpdir=tmpdir,
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gpu_collect=self.gpu_collect,
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efficient_test=self.efficient_test)
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if runner.rank == 0:
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print('\n')
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runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
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key_score = self.evaluate(runner, results)
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if self.save_best:
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self._save_ckpt(runner, key_score)
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