8b7b61b3bd
* Adding Equations, solving typos * improve _code.rst * the team rst and restuctore index.rst * fixing errors --------- Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>
59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
'''PINA Callbacks Implementations'''
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from pytorch_lightning.callbacks import Callback
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import torch
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import copy
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class MetricTracker(Callback):
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def __init__(self):
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"""
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PINA Implementation of a Lightning Callback for Metric Tracking.
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This class provides functionality to track relevant metrics during the training process.
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:ivar _collection: A list to store collected metrics after each training epoch.
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:param trainer: The trainer object managing the training process.
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:type trainer: pytorch_lightning.Trainer
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:return: A dictionary containing aggregated metric values.
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:rtype: dict
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Example:
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>>> tracker = MetricTracker()
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>>> # ... Perform training ...
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>>> metrics = tracker.metrics
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"""
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self._collection = []
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def on_train_epoch_end(self, trainer, __):
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"""
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Collect and track metrics at the end of each training epoch.
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:param trainer: The trainer object managing the training process.
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:type trainer: pytorch_lightning.Trainer
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:param _: Placeholder argument.
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:return: None
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:rtype: None
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"""
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self._collection.append(copy.deepcopy(
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trainer.logged_metrics)) # track them
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@property
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def metrics(self):
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"""
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Aggregate collected metrics during training.
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:return: A dictionary containing aggregated metric values.
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:rtype: dict
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"""
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common_keys = set.intersection(*map(set, self._collection))
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v = {
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k: torch.stack([dic[k] for dic in self._collection])
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for k in common_keys
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}
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return v
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