Spaces:
Running
Running
| #!/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 Iterator, Union | |
| import torch | |
| class EpisodeAwareSampler: | |
| def __init__( | |
| self, | |
| episode_data_index: dict, | |
| episode_indices_to_use: Union[list, None] = None, | |
| drop_n_first_frames: int = 0, | |
| drop_n_last_frames: int = 0, | |
| shuffle: bool = False, | |
| ): | |
| """Sampler that optionally incorporates episode boundary information. | |
| Args: | |
| episode_data_index: Dictionary with keys 'from' and 'to' containing the start and end indices of each episode. | |
| episode_indices_to_use: List of episode indices to use. If None, all episodes are used. | |
| Assumes that episodes are indexed from 0 to N-1. | |
| drop_n_first_frames: Number of frames to drop from the start of each episode. | |
| drop_n_last_frames: Number of frames to drop from the end of each episode. | |
| shuffle: Whether to shuffle the indices. | |
| """ | |
| indices = [] | |
| for episode_idx, (start_index, end_index) in enumerate( | |
| zip(episode_data_index["from"], episode_data_index["to"], strict=True) | |
| ): | |
| if episode_indices_to_use is None or episode_idx in episode_indices_to_use: | |
| indices.extend( | |
| range(start_index.item() + drop_n_first_frames, end_index.item() - drop_n_last_frames) | |
| ) | |
| self.indices = indices | |
| self.shuffle = shuffle | |
| def __iter__(self) -> Iterator[int]: | |
| if self.shuffle: | |
| for i in torch.randperm(len(self.indices)): | |
| yield self.indices[i] | |
| else: | |
| for i in self.indices: | |
| yield i | |
| def __len__(self) -> int: | |
| return len(self.indices) | |