76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
from abc import ABC
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import torch
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class TestEpochEndVariations(ABC):
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def test_epoch_end(self, outputs):
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"""
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Called at the end of test epoch to aggregate outputs
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:param outputs: list of individual outputs of each validation step
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:return:
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"""
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# if returned a scalar from test_step, outputs is a list of tensor scalars
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# we return just the average in this case (if we want)
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# return torch.stack(outputs).mean()
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test_loss_mean = 0
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test_acc_mean = 0
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for output in outputs:
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test_loss = self.get_output_metric(output, 'test_loss')
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# reduce manually when using dp
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if self.trainer.use_dp:
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test_loss = torch.mean(test_loss)
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test_loss_mean += test_loss
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# reduce manually when using dp
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test_acc = self.get_output_metric(output, 'test_acc')
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if self.trainer.use_dp:
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test_acc = torch.mean(test_acc)
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test_acc_mean += test_acc
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test_loss_mean /= len(outputs)
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test_acc_mean /= len(outputs)
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metrics_dict = {'test_loss': test_loss_mean, 'test_acc': test_acc_mean}
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result = {'progress_bar': metrics_dict, 'log': metrics_dict}
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return result
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def test_epoch_end__multiple_dataloaders(self, outputs):
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"""
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Called at the end of test epoch to aggregate outputs
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:param outputs: list of individual outputs of each validation step
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:return:
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"""
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# if returned a scalar from test_step, outputs is a list of tensor scalars
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# we return just the average in this case (if we want)
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# return torch.stack(outputs).mean()
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test_loss_mean = 0
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test_acc_mean = 0
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i = 0
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for dl_output in outputs:
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for output in dl_output:
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test_loss = output['test_loss']
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# reduce manually when using dp
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if self.trainer.use_dp:
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test_loss = torch.mean(test_loss)
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test_loss_mean += test_loss
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# reduce manually when using dp
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test_acc = output['test_acc']
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if self.trainer.use_dp:
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test_acc = torch.mean(test_acc)
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test_acc_mean += test_acc
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i += 1
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test_loss_mean /= i
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test_acc_mean /= i
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tqdm_dict = {'test_loss': test_loss_mean, 'test_acc': test_acc_mean}
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result = {'progress_bar': tqdm_dict}
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return result
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