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* remove the need for hparams * remove the need for hparams * remove the need for hparams * remove the need for hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * fixed * fixed * fixed * fixed * fixed * fixed * fixed * fixed * fixed * fixed * fixed * fixed * fixed * fixed * finished moco * basic * testing * todo * recurse * hparams * persist * hparams * chlog * tests * tests * tests * tests * tests * tests * review * saving * tests * tests * tests * docs * finished moco * hparams * review * Apply suggestions from code review Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com> * hparams * overwrite * transform * transform * transform * transform * cleaning * cleaning * tests * examples * examples * examples * Apply suggestions from code review Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com> * chp key * tests * Apply suggestions from code review * class * updated docs * updated docs * updated docs * updated docs * save * wip * fix * flake8 Co-authored-by: Jirka <jirka@pytorchlightning.ai> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
29 lines
747 B
Python
29 lines
747 B
Python
from torch.utils.data import DataLoader
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from tests.base.datasets import TrialMNIST
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class ModelTemplateData:
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hparams: ...
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def dataloader(self, train):
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dataset = TrialMNIST(root=self.data_root, train=train, download=True)
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loader = DataLoader(
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dataset=dataset,
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batch_size=self.batch_size,
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# test and valid shall not be shuffled
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shuffle=train,
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)
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return loader
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class ModelTemplateUtils:
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def get_output_metric(self, output, name):
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if isinstance(output, dict):
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val = output[name]
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else: # if it is 2level deep -> per dataloader and per batch
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val = sum(out[name] for out in output) / len(output)
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return val
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