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87 lines
3.4 KiB
Python
87 lines
3.4 KiB
Python
#!/usr/bin/env python
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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from collections.abc import Iterator
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import torch
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logger = logging.getLogger(__name__)
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class EpisodeAwareSampler:
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def __init__(
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self,
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dataset_from_indices: list[int],
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dataset_to_indices: list[int],
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episode_indices_to_use: list | None = None,
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drop_n_first_frames: int = 0,
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drop_n_last_frames: int = 0,
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shuffle: bool = False,
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):
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"""Sampler that optionally incorporates episode boundary information.
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Args:
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dataset_from_indices: List of indices containing the start of each episode in the dataset.
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dataset_to_indices: List of indices containing the end of each episode in the dataset.
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episode_indices_to_use: List of episode indices to use. If None, all episodes are used.
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Assumes that episodes are indexed from 0 to N-1.
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drop_n_first_frames: Number of frames to drop from the start of each episode.
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drop_n_last_frames: Number of frames to drop from the end of each episode.
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shuffle: Whether to shuffle the indices.
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"""
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if drop_n_first_frames < 0:
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raise ValueError(f"drop_n_first_frames must be >= 0, got {drop_n_first_frames}")
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if drop_n_last_frames < 0:
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raise ValueError(f"drop_n_last_frames must be >= 0, got {drop_n_last_frames}")
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indices = []
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for episode_idx, (start_index, end_index) in enumerate(
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zip(dataset_from_indices, dataset_to_indices, strict=True)
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):
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if episode_indices_to_use is None or episode_idx in episode_indices_to_use:
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ep_length = end_index - start_index
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if drop_n_first_frames + drop_n_last_frames >= ep_length:
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logger.warning(
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"Episode %d has %d frames but drop_n_first_frames=%d and "
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"drop_n_last_frames=%d removes all frames. Skipping.",
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episode_idx,
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ep_length,
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drop_n_first_frames,
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drop_n_last_frames,
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)
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continue
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indices.extend(range(start_index + drop_n_first_frames, end_index - drop_n_last_frames))
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if not indices:
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raise ValueError(
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"No valid frames remain after applying drop_n_first_frames and drop_n_last_frames. "
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"All episodes were either filtered out or had too few frames."
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)
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self.indices = indices
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self.shuffle = shuffle
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def __iter__(self) -> Iterator[int]:
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if self.shuffle:
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for i in torch.randperm(len(self.indices)):
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yield self.indices[i]
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else:
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for i in self.indices:
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yield i
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def __len__(self) -> int:
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return len(self.indices)
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