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Running on Zero
| # Copyright (c) OpenMMLab. All rights reserved. | |
| from collections import OrderedDict | |
| import numpy as np | |
| class LogBuffer: | |
| def __init__(self): | |
| self.val_history = OrderedDict() | |
| self.n_history = OrderedDict() | |
| self.output = OrderedDict() | |
| self.ready = False | |
| def clear(self) -> None: | |
| self.val_history.clear() | |
| self.n_history.clear() | |
| self.clear_output() | |
| def clear_output(self) -> None: | |
| self.output.clear() | |
| self.ready = False | |
| def update(self, vars: dict, count: int = 1) -> None: | |
| assert isinstance(vars, dict) | |
| for key, var in vars.items(): | |
| if key not in self.val_history: | |
| self.val_history[key] = [] | |
| self.n_history[key] = [] | |
| self.val_history[key].append(var) | |
| self.n_history[key].append(count) | |
| def average(self, n: int = 0) -> None: | |
| """Average latest n values or all values.""" | |
| assert n >= 0 | |
| for key in self.val_history: | |
| values = np.array(self.val_history[key][-n:]) | |
| nums = np.array(self.n_history[key][-n:]) | |
| avg = np.sum(values * nums) / np.sum(nums) | |
| self.output[key] = avg | |
| self.ready = True | |