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ddbf7de6dc
* `add_argparse_args` method fixed (argument types added) * autopep8 fixes * --gpus=0 removed from test (for ci tests) * Update pytorch_lightning/trainer/trainer.py Co-Authored-By: Joe Davison <joe@huggingface.co> * test_with_accumulate_grad_batches added * agg_and_log_metrics logic added to the base logger class * small format fix * agg metrics strategies removed (not to complicate stuff) * agg metrics: handle zero step * autopep8 * changelog upd * flake fix * metrics aggregators factored out, metrics_agg.py added + tests * metrics agg default value added * Update pytorch_lightning/loggers/metrics_agg.py Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * metrics aggregators factored out, metrics_agg.py added + tests * metrics agg default value added * Update pytorch_lightning/loggers/metrics_agg.py Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * remove .item which causes sync issues (#1254) * remove .item which causes sync issues * fixed gradient acc sched * fixed gradient acc sched * test_metrics_agg.py removed (all tested in doctrings), agg metrics refactored * test_metrics_agg.py removed (all tested in doctrings), agg metrics refactored * autopep8 * loggers base.py types fixed * test * test * metrics aggregation for loggers: each key now has a specific function (or default one) * metrics aggregation for loggers: each key now has a specific function (or default one) * docstrings upd * manual typehints removed from docstrings * batch_size decreased for test `test_with_accumulate_grad_batches` * extend running accum * refactor * fix tests * fix tests * allowed_types generator scoped * trainer.py distutils was imported twice, fixed * TensorRunningAccum refactored * TensorRunningAccum added to change log (Changed) * change log pull link added Co-authored-by: Joe Davison <joe@huggingface.co> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: William Falcon <waf2107@columbia.edu> Co-authored-by: J. Borovec <jirka.borovec@seznam.cz>
76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
import torch
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class TensorRunningAccum(object):
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"""Tracks a running accumulation values (min, max, mean) without graph
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references.
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Examples:
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>>> accum = TensorRunningAccum(5)
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>>> accum.last(), accum.mean()
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(None, None)
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>>> accum.append(torch.tensor(1.5))
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>>> accum.last(), accum.mean()
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(tensor(1.5000), tensor(1.5000))
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>>> accum.append(torch.tensor(2.5))
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>>> accum.last(), accum.mean()
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(tensor(2.5000), tensor(2.))
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>>> accum.reset()
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>>> _= [accum.append(torch.tensor(i)) for i in range(13)]
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>>> accum.last(), accum.mean(), accum.min(), accum.max()
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(tensor(12.), tensor(10.), tensor(8.), tensor(12.))
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"""
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def __init__(self, window_length: int):
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self.window_length = window_length
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self.memory = torch.Tensor(self.window_length)
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self.current_idx: int = 0
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self.last_idx: int = None
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self.rotated: bool = False
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def reset(self) -> None:
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"""Empty the accumulator."""
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self = TensorRunningAccum(self.window_length)
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def last(self):
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"""Get the last added element."""
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if self.last_idx is not None:
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return self.memory[self.last_idx]
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def append(self, x):
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"""Add an element to the accumulator."""
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# ensure same device and type
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if self.memory.device != x.device or self.memory.type() != x.type():
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x = x.to(self.memory)
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# store without grads
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with torch.no_grad():
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self.memory[self.current_idx] = x
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self.last_idx = self.current_idx
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# increase index
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self.current_idx += 1
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# reset index when hit limit of tensor
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self.current_idx = self.current_idx % self.window_length
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if self.current_idx == 0:
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self.rotated = True
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def mean(self):
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"""Get mean value from stored elements."""
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return self._agg_memory('mean')
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def max(self):
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"""Get maximal value from stored elements."""
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return self._agg_memory('max')
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def min(self):
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"""Get minimal value from stored elements."""
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return self._agg_memory('min')
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def _agg_memory(self, how: str):
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if self.last_idx is not None:
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if self.rotated:
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return getattr(self.memory, how)()
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else:
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return getattr(self.memory[:self.current_idx], how)()
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