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825cff4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | # SequenceDataset
The `robomimic.utils.dataset.SequenceDataset` class extends PyTorch's default `torch.utils.data.Dataset` to interface with our demonstration [datasets](../datasets/overview.html). The class supports accessing demonstration sub-sequences (as opposed to individual states) by index and both on-demand fetching and in-memory caching. This page walks through the key concepts of the `SequenceDataset` interface. Please refer to the official PyTorch [documentation](https://pytorch.org/docs/stable/data.html) and a short [example](https://github.com/ARISE-Initiative/robomimic/blob/master/examples/simple_train_loop.py) on how to use the `Dataset` and `DataLoader` interfaces to build a training pipeline.
Here is a sample dataset object:
```python
dataset = SequenceDataset(
hdf5_path=dataset_path,
obs_keys=( # observations we want to appear in batches
"robot0_eef_pos",
"robot0_eef_quat",
"image",
"object",
),
dataset_keys=( # can optionally specify more keys here if they should appear in batches
"actions",
"rewards",
"dones",
),
seq_length=10, # length of sub-sequence to fetch: (s_{t}, a_{t}), (s_{t+1}, a_{t+1}), ..., (s_{t+9}, a_{t+9})
frame_stack=1, # length of sub-sequence to prepend
pad_seq_length=True, # pad last obs per trajectory to ensure all sequences are sampled
pad_frame_stack=True, # pad first obs per trajectory to ensure all sequences are sampled
hdf5_cache_mode="all", # cache dataset in memory to avoid repeated file i/o
hdf5_normalize_obs=False,
filter_by_attribute=None, # can optionally provide a filter key here
)
```
- `hdf5_path`
- The absolute / relative path to the hdf5 file containing training demonstrations. See the [datasets page](../datasets/overview.html#dataset-structure) for the expected data structure.
- `obs_keys`
- A list of strings specifying which observation modalities to read from the dataset. This is typically read from the config file: our implementation pools observation keys from `config.observation.modalities.obs.low_dim` and `config.observation.modalities.obs.rgb`.
- `dataset_keys`
- Keys of non-observation data to read from a demonstration. Typically include `actions`, `rewards`, `dones`.
- `seq_length`
- Length of demonstration sub-sequence to fetch. For example, if `seq_length = 10` at time `t`, the data loader will fetch ${(s_{t}, a_{t}), (s_{t+1}, a_{t+1}), ..., (s_{t+9}, a_{t+9})}$
- `frame_stack`
- Length of sub-sequence to stack at the beginning of fetched demonstration. For example, if `frame_stack = 10` at time `t`, the data loader will fetch ${(s_{t-1}, a_{t-1}), (s_{t-2}, a_{t-2}), ..., (s_{t-9}, a_{t-9})}$. Note that the actual length of the fetched sequence is `frame_stack - 1`. This term is useful when training a model to predict `seq_length` actions from `frame_stack` observations. If training a transformer, this should be the same as context length.
- `pad_seq_length`
- Whether to allow fetching subsequence that ends beyond the sequence. For example, given a demo of length 10 and `seq_length=10`, setting `pad_seq_length=True` allows the dataset object to access subsequence at `__get_item(index=5)__` by repeating the last frame 5 times.
- `pad_frame_stack`
- Whether to allow fetching subsequence that starts before the first time step. For example, given a demo of length 10 and `frame_stack=10`, setting `pad_frame_stack=True` allows the dataset object to access subsequence at `__get_item(index=5)__` by repeating the first frame 5 times.
- `hdf5_cache_mode`
- Optionally cache the dataset in memory for faster access. The dataset supports three caching modes: `["all", "low_dim", or None]`.
- `all`: Load the entire dataset into the RAM. This mode minimizes data loading time but incurs the largest memory footprint. Recommended if the dataset is small or when working with low-dimensional observation data.
- `low_dim`: Load only the low-dimensional observations into RAM. Always use this mode when possible as loading low-dim data incurs nontrivial overhead. Low-dim observations are specified at `config.observation.modalities.obs.low_dim`.
- `None`: Always fetch data on-demand.
- `hdf5_normalize_obs`
- If `True`, normalize observations by computing the mean observation and std of each observation (in each dimension and modality), and normalizing unit mean and variance in each dimension.
- `filter_by_attribute`
- if provided, use the provided filter key to look up a subset of demonstrations to load. See the documentation on [filter keys](../datasets/overview.html#filter-keys) for more information.
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