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https://github.com/wassname/pytorch-lightning.git
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William FalconandJ. Borovec
3431c62d41
* remove error when test dataloader used in test * remove error when test dataloader used in test * remove error when test dataloader used in test * remove error when test dataloader used in test * remove error when test dataloader used in test * remove error when test dataloader used in test * fix lost model reference * remove error when test dataloader used in test * fix lost model reference * moved optimizer types * moved optimizer types * moved optimizer types * moved optimizer types * moved optimizer types * moved optimizer types * moved optimizer types * moved optimizer types * added tests for warning * fix lost model reference * fix lost model reference * added tests for warning * added tests for warning * refactoring * refactoring * fix imports * refactoring * fix imports * refactoring * fix tests * fix mnist * flake8 * review Co-authored-by: J. Borovec <jirka.borovec@seznam.cz>
23 lines
665 B
Python
23 lines
665 B
Python
from torch.utils.data import DataLoader
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from tests.base.datasets import TrialMNIST
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class ModelTemplateUtils:
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def dataloader(self, train):
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dataset = TrialMNIST(root=self.hparams.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.hparams.batch_size,
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shuffle=True
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)
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return loader
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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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