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#!/usr/bin/env python
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any
from lerobot.common.utils.utils import format_big_number
class AverageMeter:
"""
Computes and stores the average and current value
Adapted from https://github.com/pytorch/examples/blob/main/imagenet/main.py
"""
def __init__(self, name: str, fmt: str = ":f"):
self.name = name
self.fmt = fmt
self.reset()
def reset(self) -> None:
self.val = 0.0
self.avg = 0.0
self.sum = 0.0
self.count = 0.0
def update(self, val: float, n: int = 1) -> None:
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def __str__(self):
fmtstr = "{name}:{avg" + self.fmt + "}"
return fmtstr.format(**self.__dict__)
class MetricsTracker:
"""
A helper class to track and log metrics over time.
Usage pattern:
```python
# initialize, potentially with non-zero initial step (e.g. if resuming run)
metrics = {"loss": AverageMeter("loss", ":.3f")}
train_metrics = MetricsTracker(cfg, dataset, metrics, initial_step=step)
# update metrics derived from step (samples, episodes, epochs) at each training step
train_metrics.step()
# update various metrics
loss = policy.forward(batch)
train_metrics.loss = loss
# display current metrics
logging.info(train_metrics)
# export for wandb
wandb.log(train_metrics.to_dict())
# reset averages after logging
train_metrics.reset_averages()
```
"""
__keys__ = [
"_batch_size",
"_num_frames",
"_avg_samples_per_ep",
"metrics",
"steps",
"samples",
"episodes",
"epochs",
]
def __init__(
self,
batch_size: int,
num_frames: int,
num_episodes: int,
metrics: dict[str, AverageMeter],
initial_step: int = 0,
):
self.__dict__.update(dict.fromkeys(self.__keys__))
self._batch_size = batch_size
self._num_frames = num_frames
self._avg_samples_per_ep = num_frames / num_episodes
self.metrics = metrics
self.steps = initial_step
# A sample is an (observation,action) pair, where observation and action
# can be on multiple timestamps. In a batch, we have `batch_size` number of samples.
self.samples = self.steps * self._batch_size
self.episodes = self.samples / self._avg_samples_per_ep
self.epochs = self.samples / self._num_frames
def __getattr__(self, name: str) -> int | dict[str, AverageMeter] | AverageMeter | Any:
if name in self.__dict__:
return self.__dict__[name]
elif name in self.metrics:
return self.metrics[name]
else:
raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'")
def __setattr__(self, name: str, value: Any) -> None:
if name in self.__dict__:
super().__setattr__(name, value)
elif name in self.metrics:
self.metrics[name].update(value)
else:
raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'")
def step(self) -> None:
"""
Updates metrics that depend on 'step' for one step.
"""
self.steps += 1
self.samples += self._batch_size
self.episodes = self.samples / self._avg_samples_per_ep
self.epochs = self.samples / self._num_frames
def __str__(self) -> str:
display_list = [
f"step:{format_big_number(self.steps)}",
# number of samples seen during training
f"smpl:{format_big_number(self.samples)}",
# number of episodes seen during training
f"ep:{format_big_number(self.episodes)}",
# number of time all unique samples are seen
f"epch:{self.epochs:.2f}",
*[str(m) for m in self.metrics.values()],
]
return " ".join(display_list)
def to_dict(self, use_avg: bool = True) -> dict[str, int | float]:
"""
Returns the current metric values (or averages if `use_avg=True`) as a dict.
"""
return {
"steps": self.steps,
"samples": self.samples,
"episodes": self.episodes,
"epochs": self.epochs,
**{k: m.avg if use_avg else m.val for k, m in self.metrics.items()},
}
def reset_averages(self) -> None:
"""Resets average meters."""
for m in self.metrics.values():
m.reset()
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