mirror of
https://github.com/huggingface/lerobot.git
synced 2026-06-01 11:21:27 +00:00
f2b79656eb
- Updated the EnvTransition structure to use a dictionary format instead of a tuple, enhancing readability and maintainability. - Replaced instances of TransitionIndex with TransitionKey for accessing transition components. - Adjusted related processing functions and tests to accommodate the new dictionary format, ensuring consistent handling of transitions across the codebase.
77 lines
2.9 KiB
Python
77 lines
2.9 KiB
Python
#!/usr/bin/env python
|
|
|
|
# Copyright 2025 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 dataclasses import dataclass
|
|
from typing import Any
|
|
|
|
import torch
|
|
|
|
from lerobot.processor.pipeline import EnvTransition, TransitionKey
|
|
|
|
|
|
@dataclass
|
|
class DeviceProcessor:
|
|
"""Processes transitions by moving tensors to the specified device.
|
|
|
|
This processor ensures that all tensors in the transition are moved to the
|
|
specified device (CPU or GPU) before they are returned.
|
|
"""
|
|
|
|
device: str = "cpu"
|
|
|
|
def __post_init__(self):
|
|
self.non_blocking = "cuda" in self.device
|
|
|
|
def __call__(self, transition: EnvTransition) -> EnvTransition:
|
|
# Create a copy of the transition
|
|
new_transition = transition.copy()
|
|
|
|
# Process observation tensors
|
|
observation = transition.get(TransitionKey.OBSERVATION)
|
|
if observation is not None:
|
|
new_observation = {
|
|
k: v.to(self.device, non_blocking=self.non_blocking) if isinstance(v, torch.Tensor) else v
|
|
for k, v in observation.items()
|
|
}
|
|
new_transition[TransitionKey.OBSERVATION] = new_observation
|
|
|
|
# Process action tensor
|
|
action = transition.get(TransitionKey.ACTION)
|
|
if action is not None and isinstance(action, torch.Tensor):
|
|
new_transition[TransitionKey.ACTION] = action.to(self.device, non_blocking=self.non_blocking)
|
|
|
|
# Process reward tensor
|
|
reward = transition.get(TransitionKey.REWARD)
|
|
if reward is not None and isinstance(reward, torch.Tensor):
|
|
new_transition[TransitionKey.REWARD] = reward.to(self.device, non_blocking=self.non_blocking)
|
|
|
|
# Process done tensor
|
|
done = transition.get(TransitionKey.DONE)
|
|
if done is not None and isinstance(done, torch.Tensor):
|
|
new_transition[TransitionKey.DONE] = done.to(self.device, non_blocking=self.non_blocking)
|
|
|
|
# Process truncated tensor
|
|
truncated = transition.get(TransitionKey.TRUNCATED)
|
|
if truncated is not None and isinstance(truncated, torch.Tensor):
|
|
new_transition[TransitionKey.TRUNCATED] = truncated.to(
|
|
self.device, non_blocking=self.non_blocking
|
|
)
|
|
|
|
return new_transition
|
|
|
|
def get_config(self) -> dict[str, Any]:
|
|
"""Return configuration for serialization."""
|
|
return {"device": self.device}
|