55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
from abc import ABC
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import torch
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class ValidationEpochEndVariations(ABC):
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"""
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Houses all variations of validation_epoch_end steps
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"""
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def validation_epoch_end(self, outputs):
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"""
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Called at the end of validation to aggregate outputs
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Args:
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outputs: list of individual outputs of each validation step
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"""
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# if returned a scalar from validation_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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def _mean(res, key):
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# recursive mean for multilevel dicts
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return torch.stack([x[key] if isinstance(x, dict) else _mean(x, key) for x in res]).mean()
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val_loss_mean = _mean(outputs, 'val_loss')
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val_acc_mean = _mean(outputs, 'val_acc')
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metrics_dict = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
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results = {'progress_bar': metrics_dict, 'log': metrics_dict}
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return results
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def validation_epoch_end_multiple_dataloaders(self, outputs):
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"""
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Called at the end of validation to aggregate outputs
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Args:
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outputs: list of individual outputs of each validation step
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"""
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# if returned a scalar from validation_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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def _mean(res, key):
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return torch.stack([x[key] for x in res]).mean()
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pbar = {}
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logs = {}
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for dl_output_list in outputs:
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output_keys = dl_output_list[0].keys()
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output_keys = [x for x in output_keys if 'val_' in x]
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for key in output_keys:
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metric_out = _mean(dl_output_list, key)
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pbar[key] = metric_out
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logs[key] = metric_out
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results = {'progress_bar': pbar, 'log': logs}
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return results
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