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Source code for torchrl.data.datasets.vd4rl

# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations

import functools

import importlib
import json
import os
import pathlib
import shutil
import tempfile
from collections import defaultdict
from pathlib import Path
from typing import Callable, List

import numpy as np

import torch
from tensordict import PersistentTensorDict, TensorDict
from torch import multiprocessing as mp

from torchrl._utils import KeyDependentDefaultDict, logger as torchrl_logger
from torchrl.data.datasets.common import BaseDatasetExperienceReplay
from torchrl.data.datasets.utils import _get_root_dir
from torchrl.data.replay_buffers.samplers import Sampler
from torchrl.data.replay_buffers.storages import TensorStorage
from torchrl.data.replay_buffers.writers import ImmutableDatasetWriter, Writer

from torchrl.envs.transforms import Compose, Resize, ToTensorImage
from torchrl.envs.utils import _classproperty

_has_tqdm = importlib.util.find_spec("tqdm", None) is not None
_has_h5py = importlib.util.find_spec("h5py", None) is not None
_has_hf_hub = importlib.util.find_spec("huggingface_hub", None) is not None

THIS_DIR = pathlib.Path(__file__).parent


[docs]class VD4RLExperienceReplay(BaseDatasetExperienceReplay): """V-D4RL experience replay dataset. This class downloads the H5/npz data from V-D4RL and processes it in a mmap format, which makes indexing (and therefore sampling) faster. Learn more about V-D4RL here: https://arxiv.org/abs/2206.04779 The `"pixels"` entry is located at the root of the data, and all the data that is not reward, done-state, action or pixels is moved under a `"state"` node. The data format follows the :ref:`TED convention <TED-format>`. Args: dataset_id (str): the dataset to be downloaded. Must be part of VD4RLExperienceReplay.available_datasets. batch_size (int): Batch-size used during sampling. Can be overridden by `data.sample(batch_size)` if necessary. Keyword Args: root (Path or str, optional): The V-D4RL dataset root directory. The actual dataset memory-mapped files will be saved under `<root>/<dataset_id>`. If none is provided, it defaults to ``~/.cache/torchrl/vd4rl`. download (bool or str, optional): Whether the dataset should be downloaded if not found. Defaults to ``True``. Download can also be passed as ``"force"``, in which case the downloaded data will be overwritten. sampler (Sampler, optional): the sampler to be used. If none is provided a default RandomSampler() will be used. writer (Writer, optional): the writer to be used. If none is provided a default :class:`~torchrl.data.replay_buffers.writers.ImmutableDatasetWriter` will be used. collate_fn (callable, optional): merges a list of samples to form a mini-batch of Tensor(s)/outputs. Used when using batched loading from a map-style dataset. pin_memory (bool): whether pin_memory() should be called on the rb samples. prefetch (int, optional): number of next batches to be prefetched using multithreading. transform (Transform, optional): Transform to be executed when sample() is called. To chain transforms use the :class:`~torchrl.envs.transforms.transforms.Compose` class. split_trajs (bool, optional): if ``True``, the trajectories will be split along the first dimension and padded to have a matching shape. To split the trajectories, the ``"done"`` signal will be used, which is recovered via ``done = truncated | terminated``. In other words, it is assumed that any ``truncated`` or ``terminated`` signal is equivalent to the end of a trajectory. For some datasets from ``D4RL``, this may not be true. It is up to the user to make accurate choices regarding this usage of ``split_trajs``. Defaults to ``False``. totensor (bool, optional): if ``True``, a :class:`~torchrl.envs.transforms.ToTensorImage` transform will be included in the transform list (if not automatically detected). Defaults to ``True``. image_size (int, list of ints or None): if not ``None``, this argument will be used to create a :class:`~torchrl.envs.transforms.Resize` transform that will be appended to the transform list. Supports `int` types (square resizing) or a list/tuple of `int` (rectangular resizing). Defaults to ``None`` (no resizing). num_workers (int, optional): the number of workers to download the files. Defaults to ``0`` (no multiprocessing). Attributes: available_datasets: a list of accepted entries to be downloaded. These names correspond to the directory path in the huggingface dataset repository. If possible, the list will be dynamically retrieved from huggingface. If no internet connection is available, it a cached version will be used. .. note:: Since not all experience replay have start and stop signals, we do not mark the episodes in the retrieved dataset. Examples: >>> import torch >>> torch.manual_seed(0) >>> from torchrl.data.datasets import VD4RLExperienceReplay >>> d = VD4RLExperienceReplay("main/walker_walk/random/64px", batch_size=32, ... image_size=50) >>> for batch in d: ... break >>> print(batch) TensorDict( fields={ action: Tensor(shape=torch.Size([32, 6]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.bool, is_shared=False), index: Tensor(shape=torch.Size([32]), device=cpu, dtype=torch.int64, is_shared=False), is_init: Tensor(shape=torch.Size([32]), device=cpu, dtype=torch.bool, is_shared=False), next: TensorDict( fields={ done: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.bool, is_shared=False), observation: TensorDict( fields={ height: Tensor(shape=torch.Size([32]), device=cpu, dtype=torch.float32, is_shared=False), orientations: Tensor(shape=torch.Size([32, 14]), device=cpu, dtype=torch.float32, is_shared=False), velocity: Tensor(shape=torch.Size([32, 9]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([32]), device=cpu, is_shared=False), pixels: Tensor(shape=torch.Size([32, 3, 50, 50]), device=cpu, dtype=torch.float32, is_shared=False), reward: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.float32, is_shared=False), terminated: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.bool, is_shared=False), truncated: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, batch_size=torch.Size([32]), device=cpu, is_shared=False), observation: TensorDict( fields={ height: Tensor(shape=torch.Size([32]), device=cpu, dtype=torch.float32, is_shared=False), orientations: Tensor(shape=torch.Size([32, 14]), device=cpu, dtype=torch.float32, is_shared=False), velocity: Tensor(shape=torch.Size([32, 9]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([32]), device=cpu, is_shared=False), pixels: Tensor(shape=torch.Size([32, 3, 50, 50]), device=cpu, dtype=torch.float32, is_shared=False), terminated: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.bool, is_shared=False), truncated: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.bool, is_shared=False)}, batch_size=torch.Size([32]), device=cpu, is_shared=False) """ def __init__( self, dataset_id, batch_size: int, *, root: str | Path | None = None, download: bool = True, sampler: Sampler | None = None, writer: Writer | None = None, collate_fn: Callable | None = None, pin_memory: bool = False, prefetch: int | None = None, transform: "torchrl.envs.Transform" | None = None, # noqa-F821 split_trajs: bool = False, totensor: bool = True, image_size: int | List[int] | None = None, num_workers: int = 0, **env_kwargs, ): if not _has_h5py or not _has_hf_hub: raise ImportError( "h5py and huggingface_hub are required for V-D4RL datasets." ) if dataset_id not in self.available_datasets: raise ValueError( f"The dataset_id {dataset_id} isn't part of the accepted datasets. " f"To check which dataset can be downloaded, call `{type(self)}.available_datasets`." ) self.dataset_id = dataset_id if root is None: root = _get_root_dir("vd4rl") os.makedirs(root, exist_ok=True) self.root = root self.split_trajs = split_trajs self.download = download self.num_workers = num_workers if self.download == "force" or (self.download and not self._is_downloaded()): if self.download == "force": try: if os.path.exists(self.data_path_root): shutil.rmtree(self.data_path_root) if self.data_path != self.data_path_root: shutil.rmtree(self.data_path) except FileNotFoundError: pass storage = self._download_and_preproc( dataset_id, data_path=self.data_path, num_workers=self.num_workers ) elif self.split_trajs and not os.path.exists(self.data_path): storage = self._make_split() else: storage = self._load() if totensor and transform is None: transform = ToTensorImage( in_keys=["pixels", ("next", "pixels")], shape_tolerant=True ) elif totensor and ( not isinstance(transform, Compose) or not any(isinstance(t, ToTensorImage) for t in transform) ): transform = Compose( transform, ToTensorImage( in_keys=["pixels", ("next", "pixels")], shape_tolerant=True ), ) if image_size is not None: transform = Compose( transform, Resize(image_size, in_keys=["pixels", ("next", "pixels")]) ) storage = TensorStorage(storage) if writer is None: writer = ImmutableDatasetWriter() super().__init__( storage=storage, sampler=sampler, writer=writer, collate_fn=collate_fn, pin_memory=pin_memory, prefetch=prefetch, transform=transform, batch_size=batch_size, ) @classmethod def _parse_datasets(cls): from huggingface_hub import HfApi dataset = HfApi().dataset_info("conglu/vd4rl") sibs = defaultdict(list) for sib in dataset.siblings: if sib.rfilename.endswith("npz") or sib.rfilename.endswith("hdf5"): path = Path(sib.rfilename) sibs[path.parent].append(path) return sibs @classmethod def _hf_hub_download(cls, subfolder, filename, *, tmpdir): from huggingface_hub import hf_hub_download return hf_hub_download( "conglu/vd4rl", subfolder=subfolder, filename=filename, repo_type="dataset", cache_dir=str(tmpdir), ) @classmethod def _download_and_preproc(cls, dataset_id, data_path, num_workers): tds = [] with tempfile.TemporaryDirectory() as tmpdir: sibs = cls._parse_datasets() total_steps = 0 paths_to_proc = [] files_to_proc = [] for path in sibs: if dataset_id not in str(path): continue for file in sibs[path]: paths_to_proc.append(str(path)) files_to_proc.append(str(file.parts[-1])) func = functools.partial(cls._hf_hub_download, tmpdir=tmpdir) if num_workers > 0: with mp.Pool(num_workers) as pool: files = pool.starmap( func, zip(paths_to_proc, files_to_proc), ) files = list(files) else: files = [ func(subfolder, filename) for (subfolder, filename) in zip(paths_to_proc, files_to_proc) ] torchrl_logger.info("Downloaded, processing files") if _has_tqdm: import tqdm pbar = tqdm.tqdm(files) else: pbar = files for local_path in pbar: if _has_tqdm: pbar.set_description(f"file={local_path}") # we memmap temporarily the files for faster access later if local_path.endswith("hdf5"): td = ( PersistentTensorDict.from_h5(local_path) .to_tensordict() .memmap(num_threads=32) ) else: td = _from_npz(local_path).memmap(num_threads=32) td.unlock_() if total_steps == 0: tdc = cls._process_data(td.clone()) td_save = tdc[0] tds.append(td) total_steps += td.shape[0] # From this point, the local paths are non needed anymore td_save = td_save.expand(total_steps).memmap_like(data_path, num_threads=32) torchrl_logger.info(f"Saved tensordict: {td_save}") idx0 = 0 idx1 = 0 while len(files): _ = files.pop(0) td = tds.pop(0) td = cls._process_data(td) idx1 += td.shape[0] td_save[idx0:idx1] = td idx0 = idx1 return td_save @classmethod def _process_data(cls, td: TensorDict): for name in list(td.keys()): # move remaining data if name not in _NAME_MATCH: td.rename_key_(name, ("state", name)) elif name != _NAME_MATCH[name]: td.rename_key_(name, _NAME_MATCH[name]) if ("next", "reward") in td.keys(True): td.set(("next", "reward"), td.get(("next", "reward")).unsqueeze(-1)) if ("next", "done") in td.keys(True) and ("next", "terminated") in td.keys( True ): # first unsqueeze td.set(("next", "done"), td.get(("next", "done")).unsqueeze(-1)) td.set(("next", "terminated"), td.get(("next", "terminated")).unsqueeze(-1)) # create root vals td.set("done", torch.zeros_like(td.get(("next", "done")))) td.set("terminated", torch.zeros_like(td.get(("next", "terminated")))) # Add truncated td.set( ("next", "truncated"), td.get(("next", "done")) & ~td.get(("next", "terminated")), ) td.set("truncated", torch.zeros_like(td.get(("next", "truncated")))) pixels = td.get("pixels") subtd = td._get_sub_tensordict(slice(0, -1)) subtd.set(("next", "pixels"), pixels[1:], inplace=True) state = td.get("state", None) if state is not None: subtd.set(("next", "state"), state[1:], inplace=True) return td @_classproperty def available_datasets(cls): return cls._available_datasets() @classmethod def _available_datasets(cls): # try to gather paths from hf try: sibs = cls._parse_datasets() return [str(path)[6:] for path in sibs] except Exception: # return the default datasets with open(THIS_DIR / "vd4rl.json", "r") as file: return json.load(file) def _make_split(self): from torchrl.collectors.utils import split_trajectories td_data = TensorDict.load_memmap(self.data_path_root) td_data = split_trajectories(td_data).memmap_(self.data_path) return td_data def _load(self): return TensorDict.load_memmap(self.data_path) @property def data_path(self): if self.split_trajs: return Path(self.root) / (self.dataset_id + "_split") return self.data_path_root @property def data_path_root(self): return Path(self.root) / self.dataset_id def _is_downloaded(self): return os.path.exists(self.data_path_root)
def _from_npz(npz_path): npz = np.load(npz_path) npz_dict = {file: npz[file] for file in npz.files} return TensorDict.from_dict(npz_dict) _NAME_MATCH = KeyDependentDefaultDict(lambda x: x) _NAME_MATCH.update( { "is_first": "is_init", "is_last": ("next", "done"), "is_terminal": ("next", "terminated"), "reward": ("next", "reward"), "image": "pixels", "observation": "pixels", "discount": "discount", "action": "action", } )

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