79 lines
2.7 KiB
Python
79 lines
2.7 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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# alternate between tensor and scalar
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if self.current_epoch % 2 == 0:
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val_loss_mean = val_loss_mean.item()
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val_acc_mean = val_acc_mean.item()
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metrics_dict = {'val_loss': val_loss_mean, 'val_acc': val_acc_mean}
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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_return_none(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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return None
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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 = {
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'val_loss': torch.stack([v for k, v in pbar.items() if k.startswith('val_loss')]).mean(),
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'progress_bar': pbar,
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'log': logs,
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}
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return results
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