75 lines
2.5 KiB
Python
75 lines
2.5 KiB
Python
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import os.path as osp
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from mmcv.runner import Hook
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from torch.utils.data import DataLoader
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class EvalHook(Hook):
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"""Evaluation hook.
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Args:
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dataloader (DataLoader): A PyTorch dataloader.
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interval (int): Evaluation interval (by epochs). Default: 1.
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"""
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def __init__(self, dataloader, interval=1, **eval_kwargs):
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if not isinstance(dataloader, DataLoader):
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raise TypeError('dataloader must be a pytorch DataLoader, but got'
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f' {type(dataloader)}')
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self.dataloader = dataloader
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self.interval = interval
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self.eval_kwargs = eval_kwargs
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def after_train_epoch(self, runner):
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if not self.every_n_epochs(runner, self.interval):
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return
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from mmcls.apis import single_gpu_test
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results = single_gpu_test(runner.model, self.dataloader, show=False)
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self.evaluate(runner, results)
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def evaluate(self, runner, results):
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eval_res = self.dataloader.dataset.evaluate(
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results, logger=runner.logger, **self.eval_kwargs)
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for name, val in eval_res.items():
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runner.log_buffer.output[name] = val
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runner.log_buffer.ready = True
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class DistEvalHook(EvalHook):
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"""Distributed evaluation hook.
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Args:
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dataloader (DataLoader): A PyTorch dataloader.
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interval (int): Evaluation interval (by epochs). Default: 1.
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tmpdir (str | None): Temporary directory to save the results of all
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processes. Default: None.
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gpu_collect (bool): Whether to use gpu or cpu to collect results.
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Default: False.
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"""
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def __init__(self,
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dataloader,
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interval=1,
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gpu_collect=False,
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**eval_kwargs):
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if not isinstance(dataloader, DataLoader):
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raise TypeError('dataloader must be a pytorch DataLoader, but got '
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f'{type(dataloader)}')
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self.dataloader = dataloader
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self.interval = interval
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self.gpu_collect = gpu_collect
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self.eval_kwargs = eval_kwargs
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def after_train_epoch(self, runner):
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if not self.every_n_epochs(runner, self.interval):
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return
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from mmcls.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=osp.join(runner.work_dir, '.eval_hook'),
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gpu_collect=self.gpu_collect)
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if runner.rank == 0:
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print('\n')
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self.evaluate(runner, results)
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