lightning/pytorch_lightning/trainer/supporters.py

79 lines
2.4 KiB
Python

from typing import Optional
import torch
class TensorRunningAccum(object):
"""Tracks a running accumulation values (min, max, mean) without graph
references.
Examples:
>>> accum = TensorRunningAccum(5)
>>> accum.last(), accum.mean()
(None, None)
>>> accum.append(torch.tensor(1.5))
>>> accum.last(), accum.mean()
(tensor(1.5000), tensor(1.5000))
>>> accum.append(torch.tensor(2.5))
>>> accum.last(), accum.mean()
(tensor(2.5000), tensor(2.))
>>> accum.reset()
>>> _= [accum.append(torch.tensor(i)) for i in range(13)]
>>> accum.last(), accum.mean(), accum.min(), accum.max()
(tensor(12.), tensor(10.), tensor(8.), tensor(12.))
"""
def __init__(self, window_length: int):
self.window_length = window_length
self.memory = torch.Tensor(self.window_length)
self.current_idx: int = 0
self.last_idx: Optional[int] = None
self.rotated: bool = False
def reset(self) -> None:
"""Empty the accumulator."""
self = TensorRunningAccum(self.window_length)
def last(self):
"""Get the last added element."""
if self.last_idx is not None:
return self.memory[self.last_idx]
def append(self, x):
"""Add an element to the accumulator."""
# ensure same device and type
if self.memory.device != x.device or self.memory.type() != x.type():
x = x.to(self.memory)
# store without grads
with torch.no_grad():
self.memory[self.current_idx] = x
self.last_idx = self.current_idx
# increase index
self.current_idx += 1
# reset index when hit limit of tensor
self.current_idx = self.current_idx % self.window_length
if self.current_idx == 0:
self.rotated = True
def mean(self):
"""Get mean value from stored elements."""
return self._agg_memory('mean')
def max(self):
"""Get maximal value from stored elements."""
return self._agg_memory('max')
def min(self):
"""Get minimal value from stored elements."""
return self._agg_memory('min')
def _agg_memory(self, how: str):
if self.last_idx is not None:
if self.rotated:
return getattr(self.memory, how)()
else:
return getattr(self.memory[:self.current_idx], how)()