From 354215ef6b4d2d80801882710b6c3b32ddce9195 Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Wed, 6 Dec 2023 18:11:04 -0800 Subject: [PATCH 01/25] "tasks" -> "task" --- finetune.py | 2 +- orca/data/utils/bc_goal_relabeling.py | 12 ++++----- orca/data/utils/task_augmentation.py | 36 +++++++++++++-------------- orca/utils/pretrained_utils.py | 16 ++++++------ orca/utils/train_callbacks.py | 8 +++--- orca/utils/train_utils.py | 8 +++--- orca/utils/visualization_lib.py | 6 ++--- train.py | 4 +-- 8 files changed, 46 insertions(+), 46 deletions(-) diff --git a/finetune.py b/finetune.py index 403f80e1..d3872292 100644 --- a/finetune.py +++ b/finetune.py @@ -264,7 +264,7 @@ def loss_fn(params, state, batch, rng, train=True): model = model_def.bind({"params": params}, rngs={"dropout": rng}) transformer_embeddings = model.orca_transformer( batch["observation"], - batch["tasks"], + batch["task"], batch["observation"]["pad_mask"], train=train, ) diff --git a/orca/data/utils/bc_goal_relabeling.py b/orca/data/utils/bc_goal_relabeling.py index 1838fc7f..d73b3a6d 100644 --- a/orca/data/utils/bc_goal_relabeling.py +++ b/orca/data/utils/bc_goal_relabeling.py @@ -20,12 +20,12 @@ def uniform(traj): # sometimes there are floating-point errors that cause an out-of-bounds goal_idxs = tf.minimum(goal_idxs, traj_len - 1) - traj["tasks"] = tf.nest.map_structure( + traj["task"] = tf.nest.map_structure( lambda x: tf.gather(x, goal_idxs), traj["observation"], ) - traj["tasks"]["goal_timestep"] = goal_idxs + 1 - traj["tasks"]["end_timestep"] = tf.ones_like(goal_idxs) * traj_len + traj["task"]["goal_timestep"] = goal_idxs + 1 + traj["task"]["end_timestep"] = tf.ones_like(goal_idxs) * traj_len return traj @@ -35,11 +35,11 @@ def no_image_conditioning(traj): Relabels with empty goal images. """ traj_len = tf.shape(tf.nest.flatten(traj["observation"])[0])[0] - traj["tasks"] = tf.nest.map_structure( + traj["task"] = tf.nest.map_structure( lambda x: tf.zeros_like(x), traj["observation"], ) - traj["tasks"]["goal_timestep"] = tf.fill([traj_len], traj_len) - traj["tasks"]["end_timestep"] = tf.fill([traj_len], traj_len) + traj["task"]["goal_timestep"] = tf.fill([traj_len], traj_len) + traj["task"]["end_timestep"] = tf.fill([traj_len], traj_len) return traj diff --git a/orca/data/utils/task_augmentation.py b/orca/data/utils/task_augmentation.py index b63c81d9..3254b9d1 100644 --- a/orca/data/utils/task_augmentation.py +++ b/orca/data/utils/task_augmentation.py @@ -13,51 +13,51 @@ def delete_task_conditioning( delete_key_groups_probs: List[Tuple[List[str], float]], ): """ - Randomly chooses one group, and deletes all the keys in the tasks dictionary matching this pattern. + Randomly chooses one group, and deletes all the keys in the task dictionary matching this pattern. - :param traj: A dictionary containing trajectory data. should have a "tasks" key. + :param traj: A dictionary containing trajectory data. should have a "task" key. :param switch_key_groups_probs: A list of tuples, where each tuple contains a list of patterns and their probability. :return: A dictionary with keys zeroed out according to the specified probabilities. """ - if tf.math.reduce_all(traj["tasks"]["language_instruction"] == ""): + if tf.math.reduce_all(traj["task"]["language_instruction"] == ""): return traj - tasks = traj["tasks"] - new_tasks = tasks.copy() + task = traj["task"] + new_task = task.copy() delete_probs = [prob for _, prob in delete_key_groups_probs] delete_group_idx = tf.random.categorical(tf.math.log([delete_probs]), 1)[0, 0] - image_keys = [key for key in tasks.keys() if "image" in key] + image_keys = [key for key in task.keys() if "image" in key] for i, (delete_key_patterns, _) in enumerate(delete_key_groups_probs): matching_keys = [ key - for key in tasks.keys() + for key in task.keys() if any(fnmatch(key, pattern) for pattern in delete_key_patterns) ] # When no goal images are present, the goal timestep becomes the final timestep if all([image_key in matching_keys for image_key in image_keys]): - new_tasks["goal_timestep"] = tf.where( + new_task["goal_timestep"] = tf.where( i == delete_group_idx, - tasks["end_timestep"], - tasks["goal_timestep"], + task["end_timestep"], + task["goal_timestep"], ) for key in matching_keys: - new_tasks[key] = tf.where( + new_task[key] = tf.where( i == delete_group_idx, - tf.zeros_like(tasks[key]) - if tf.debugging.is_numeric_tensor(tasks[key]) + tf.zeros_like(task[key]) + if tf.debugging.is_numeric_tensor(task[key]) else "", - tasks[key], + task[key], ) - new_tasks["pad_mask_dict"][key] = tf.where( + new_task["pad_mask_dict"][key] = tf.where( i == delete_group_idx, - tf.zeros_like(tasks["pad_mask_dict"][key]), - new_tasks["pad_mask_dict"][key], + tf.zeros_like(task["pad_mask_dict"][key]), + new_task["pad_mask_dict"][key], ) - traj["tasks"] = new_tasks + traj["task"] = new_task return traj diff --git a/orca/utils/pretrained_utils.py b/orca/utils/pretrained_utils.py index a94e696c..67130327 100644 --- a/orca/utils/pretrained_utils.py +++ b/orca/utils/pretrained_utils.py @@ -85,7 +85,7 @@ def create_tasks(self, goals: Data = None, texts: Optional[Sequence[str]] = None lambda example: jnp.zeros( (batch_size, *example.shape[1:]), dtype=example.dtype ), - self.example_batch["tasks"], + self.example_batch["task"], ) if texts is None: @@ -94,7 +94,7 @@ def create_tasks(self, goals: Data = None, texts: Optional[Sequence[str]] = None if self.text_processor is not None: tasks["language_instruction"] = self.text_processor.encode(texts) - _verify_shapes(tasks, self.example_batch["tasks"], starting_dim=1) + _verify_shapes(tasks, self.example_batch["task"], starting_dim=1) return tasks def run_transformer(self, observations, tasks, pad_mask, train=False): @@ -103,13 +103,13 @@ def run_transformer(self, observations, tasks, pad_mask, train=False): observations: dictionary of arrays of shape (batch_size, window_size, *shape). Shape must be consistent with self.example_batch["observation"] tasks: dict of tasks of shape (batch_size, *shape) - Shape must be consistent with self.example_batch["tasks"] + Shape must be consistent with self.example_batch["task"] pad_mask: (batch_size, window_size) Boolean mask that is False when the timestep corresponds to padding train: whether to run in train mode *args, **kwargs: Additional arguments for transformer or model.apply """ _verify_shapes(observations, self.example_batch["observation"], starting_dim=2) - _verify_shapes(tasks, self.example_batch["tasks"], starting_dim=1) + _verify_shapes(tasks, self.example_batch["task"], starting_dim=1) return self.orca_transformer(observations, tasks, pad_mask, train=train) @@ -195,8 +195,8 @@ def load_pretrained( ) logging.info("Checking task definition:...") changed_input = changed_input or _verify_shapes( - example_batch["tasks"], - orig_example_batch["tasks"], + example_batch["task"], + orig_example_batch["task"], starting_dim=1, raise_error=False, ) @@ -232,7 +232,7 @@ def load_pretrained( partial(orig_model_def.init, train=False), rng, orig_example_batch["observation"], - orig_example_batch["tasks"], + orig_example_batch["task"], orig_example_batch["observation"]["pad_mask"], )["params"] all_steps = orbax.checkpoint.utils.checkpoint_steps(checkpoint_path) @@ -265,7 +265,7 @@ def _init(): return model_def.init( rng, example_batch["observation"], - example_batch["tasks"], + example_batch["task"], example_batch["observation"]["pad_mask"], train=False, ) diff --git a/orca/utils/train_callbacks.py b/orca/utils/train_callbacks.py index 6d55420f..a9e1fbf8 100644 --- a/orca/utils/train_callbacks.py +++ b/orca/utils/train_callbacks.py @@ -235,20 +235,20 @@ def eval_step(state, batch): all_tasks = {} if "base" in self.modes_to_evaluate: - all_tasks["base"] = batch["tasks"] + all_tasks["base"] = batch["task"] if "image_conditioned" in self.modes_to_evaluate: all_tasks["image_conditioned"] = remove_text( - batch["tasks"], self.zero_text + batch["task"], self.zero_text ) if "text_conditioned" in self.modes_to_evaluate: all_tasks["text_conditioned"] = remove_images(batch["tasks"]) if "unconditioned" in self.modes_to_evaluate: all_tasks["unconditioned"] = remove_text( - remove_images(batch["tasks"]), self.zero_text + remove_images(batch["task"]), self.zero_text ) return { - k: loss_fn_partial(batch=flax.core.copy(batch, {"tasks": tasks}))[1] + k: loss_fn_partial(batch=flax.core.copy(batch, {"task": tasks}))[1] for k, tasks in all_tasks.items() } diff --git a/orca/utils/train_utils.py b/orca/utils/train_utils.py index ba4b6756..152dfbee 100644 --- a/orca/utils/train_utils.py +++ b/orca/utils/train_utils.py @@ -394,12 +394,12 @@ def process_text(batch: Data, text_processor: Optional[TextProcessor]) -> Data: If the text processor is None, removes language entirely from the tasks. Expects batch to be a nested dictionary, where - batch["tasks"]["language_instruction"] is a sequence of byte strings + batch["task"]["language_instruction"] is a sequence of byte strings """ if text_processor is None: - batch["tasks"].pop("language_instruction") + batch["task"].pop("language_instruction") else: - batch["tasks"]["language_instruction"] = text_processor.encode( - [s.decode("utf-8") for s in batch["tasks"]["language_instruction"]] + batch["task"]["language_instruction"] = text_processor.encode( + [s.decode("utf-8") for s in batch["task"]["language_instruction"]] ) return batch diff --git a/orca/utils/visualization_lib.py b/orca/utils/visualization_lib.py index 052d04e4..f47788f6 100644 --- a/orca/utils/visualization_lib.py +++ b/orca/utils/visualization_lib.py @@ -95,10 +95,10 @@ def run_policy_on_trajectory(policy_fn, traj, *, text_processor=None): ) if text_processor: tasks["language_instruction"] = text_processor.encode( - [s.decode("utf-8") for s in traj["tasks"]["language_instruction"]] + [s.decode("utf-8") for s in traj["task"]["language_instruction"]] ) tasks["pad_mask_dict"]["language_instruction"] = np.array( - [len(s.decode("utf-8")) > 0 for s in traj["tasks"]["language_instruction"]] + [len(s.decode("utf-8")) > 0 for s in traj["task"]["language_instruction"]] ) actions = policy_fn(traj["observation"], tasks) @@ -582,7 +582,7 @@ def plot_trajectory_overview_mpl( if chunk_idx == 0 and (act.shape[0] // chunk_length) <= 20: ax.axvline(t, color="red", linestyle="--", alpha=0.2) ax.set_ylabel(f"dim {i}") - fig.suptitle(traj["tasks"]["language_instruction"][0].decode("utf-8")) + fig.suptitle(traj["task"]["language_instruction"][0].decode("utf-8")) return wandb.Image(wandb_figure.image) diff --git a/train.py b/train.py index a5d6335c..b0f779d8 100644 --- a/train.py +++ b/train.py @@ -220,7 +220,7 @@ def process_batch(batch): construct_rng = jax.random.PRNGKey(FLAGS.config.seed) model_init_args = ( example_batch["observation"], - example_batch["tasks"], + example_batch["task"], example_batch["observation"]["pad_mask"], ) print( @@ -299,7 +299,7 @@ def loss_fn(params, state, batch, rng, train=True): model = model_def.bind({"params": params}, rngs={"dropout": rng}) transformer_embeddings = model.orca_transformer( batch["observation"], - batch["tasks"], + batch["task"], batch["observation"]["pad_mask"], train=train, ) From 782e27f2286580e110df0a8af1adfd09419519ee Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Wed, 6 Dec 2023 19:31:25 -0800 Subject: [PATCH 02/25] First pass at final dataset refactor --- config.py | 6 +- orca/data/dataset.py | 376 ++++++++++++------------ orca/data/dataset_transforms.py | 116 -------- orca/data/oxe/oxe_dataset_configs.py | 21 +- orca/data/oxe/oxe_dataset_mixes.py | 45 +-- orca/data/oxe/oxe_dataset_transforms.py | 2 - orca/data/standardization_transforms.py | 116 ++++++++ orca/data/utils/data_utils.py | 137 +++------ orca/utils/train_callbacks.py | 6 +- orca/utils/visualization_lib.py | 9 +- tests/debug_config.py | 6 +- 11 files changed, 386 insertions(+), 454 deletions(-) delete mode 100644 orca/data/dataset_transforms.py create mode 100644 orca/data/standardization_transforms.py diff --git a/config.py b/config.py index 12a8c8e0..572e7894 100644 --- a/config.py +++ b/config.py @@ -4,6 +4,8 @@ from ml_collections import ConfigDict from ml_collections.config_dict import FieldReference, placeholder +from orca.data.utils.data_utils import NormalizationType + def update_config(config, **kwargs): updates = ConfigDict(kwargs) @@ -104,7 +106,7 @@ def get_config( def get_dataset_config(modality="multimodal", window_size=1): - normalization_type = "normal" + normalization_type = NormalizationType.NORMAL if modality == "multimodal": task_augmentation = dict( task_augment_strategy="delete_task_conditioning", @@ -154,11 +156,11 @@ def get_dataset_config(modality="multimodal", window_size=1): "random_hue", ], ), + num_parallel_calls=200, **task_augmentation, ), "traj_transform_threads": 48, # shared between all datasets "traj_read_threads": 48, # shared between all datasets - "frame_transform_threads": 200, # not shared between datasets "shuffle_buffer_size": 100000, # shared between all datasets "batch_size": 1024, "balance_weights": True, diff --git a/orca/data/dataset.py b/orca/data/dataset.py index b1d73369..fcf307d6 100644 --- a/orca/data/dataset.py +++ b/orca/data/dataset.py @@ -1,5 +1,6 @@ import copy from functools import partial +import inspect import json from typing import Callable, List, Optional, Sequence, Tuple, Union @@ -9,18 +10,15 @@ import tensorflow as tf import tensorflow_datasets as tfds -from orca.data.dataset_transforms import RLDS_TRAJECTORY_MAP_TRANSFORMS +from orca.data.standardization_transforms import RLDS_STANDARDIZATION_TRANSFORMS from orca.data.utils import bc_goal_relabeling, task_augmentation from orca.data.utils.data_utils import ( - action_encoding_length, - ActionEncoding, allocate_threads, get_dataset_statistics, - make_zero_actions, + make_neutral_actions, + NormalizationType, normalize_action_and_proprio, pprint_data_mixture, - state_encoding_length, - StateEncoding, tree_map, ) @@ -29,7 +27,6 @@ def _chunk_act_obs( traj, window_size, additional_action_window_size=0, - action_encoding: ActionEncoding = ActionEncoding.EEF_POS, ): """ Chunks actions and observations into the given window_size. @@ -51,8 +48,8 @@ def _chunk_act_obs( floored_chunk_indices = tf.maximum(chunk_indices, 0) - if "tasks" in traj: - goal_timestep = traj["tasks"]["goal_timestep"] + if "task" in traj: + goal_timestep = traj["task"]["goal_timestep"] else: goal_timestep = tf.fill([traj_len], traj_len, dtype=tf.int32) @@ -70,10 +67,10 @@ def _chunk_act_obs( # Actions past the goal timestep become no-ops action_past_goal = action_chunk_indices > goal_timestep[:, None] - 1 - zero_actions = make_zero_actions(traj["action"], action_encoding) - traj["action"] = tf.where( - action_past_goal[:, :, None], zero_actions, traj["action"] - ) + # zero_actions = make_neutral_actions(traj["action"], action_encoding) + # traj["action"] = tf.where( + # action_past_goal[:, :, None], zero_actions, traj["action"] + # ) return traj @@ -187,17 +184,15 @@ def apply_trajectory_transforms( goal_relabeling_kwargs: dict = {}, window_size: int = 1, additional_action_window_size: int = 0, - action_encoding: ActionEncoding = ActionEncoding.EEF_POS, subsample_length: Optional[int] = None, skip_unlabeled: bool = False, max_action: Optional[float] = None, max_proprio: Optional[float] = None, num_parallel_calls: int = tf.data.AUTOTUNE, ) -> dl.DLataset: - """ - Applies common transforms that happen at a trajectory level. Such transforms are usually some - sort of "relabeling" (e.g. filtering, chunking, adding goals, dropping keys). Transforms that - happen in this function should have the following properties: + """Applies common transforms that happen at a trajectory level. Such transforms are usually some sort of + "relabeling" (e.g. filtering, chunking, adding goals, dropping keys). Transforms that happen in this + function should have the following properties: - They require access to an entire trajectory (i.e. they cannot be applied in a frame-wise manner). - They are generally not CPU-intensive, mostly involving moving and copying data. @@ -205,14 +200,13 @@ def apply_trajectory_transforms( Args: dataset (dl.DLataset): The dataset to transform. - train (bool): Whether the dataset is for training (affects task augmentation). + train (bool): Whether the dataset is for training (affects subsampling). goal_relabeling_strategy (str, optional): The goal relabeling strategy to use, or None for no goal relabeling. See `bc_goal_relabeling.py`. goal_relabeling_kwargs (dict, optional): Additional keyword arguments to pass to the goal relabeling function. window_size (int, optional): The length of the snippets that trajectories are chunked into. additional_action_window_size (int, optional): The number of additional actions beyond window_size to include in the chunked actions. - action_encoding (ActionEncoding): type of action encoding used, e.g. joint delta vs EEF delta. subsample_length (int, optional): If provided, trajectories longer than this will be subsampled to this length (after goal relabeling). skip_unlabeled (bool, optional): Whether to skip trajectories with no language labels. @@ -222,7 +216,7 @@ def apply_trajectory_transforms( of *any* transition has an absolute value larger than this will be skipped. num_parallel_calls (int, optional): number of parallel calls for map operations. Default to AUTOTUNE. """ - if skip_unlabeled: + if skip_unlabeled and "language_instruction" in dataset.element_spec: dataset = dataset.filter( lambda x: tf.math.reduce_any(x["language_instruction"] != "") ) @@ -232,17 +226,19 @@ def apply_trajectory_transforms( lambda x: tf.math.reduce_all(tf.math.abs(x["action"]) <= max_action) ) - if max_proprio is not None: + if max_proprio is not None and "proprio" in dataset.element_spec["observation"]: dataset = dataset.filter( lambda x: tf.math.reduce_all( tf.math.abs(x["observation"]["proprio"]) <= max_proprio ) ) - dataset = dataset.map(_add_pad_mask_dict, num_parallel_calls) - # adds the "tasks" key + # marks which observations are padding + dataset = dataset.traj_map(_add_pad_mask_dict, num_parallel_calls) + + # adds the "task" key if goal_relabeling_strategy is not None: - dataset = dataset.map( + dataset = dataset.traj_map( partial( getattr(bc_goal_relabeling, goal_relabeling_strategy), **goal_relabeling_kwargs, @@ -250,26 +246,29 @@ def apply_trajectory_transforms( num_parallel_calls, ) - def move_language_instruction_to_tasks(traj): - traj["tasks"]["language_instruction"] = traj.pop("language_instruction") - traj["tasks"]["pad_mask_dict"]["language_instruction"] = ( - tf.strings.length(traj["tasks"]["language_instruction"]) != 0 - ) - return traj + if "language_instruction" in dataset.element_spec: - dataset = dataset.map(move_language_instruction_to_tasks, num_parallel_calls) + def move_language_instruction_to_task(traj): + traj["task"]["language_instruction"] = traj.pop("language_instruction") + traj["task"]["pad_mask_dict"]["language_instruction"] = ( + tf.strings.length(traj["task"]["language_instruction"]) != 0 + ) + return traj + + dataset = dataset.traj_map( + move_language_instruction_to_task, num_parallel_calls + ) if train and subsample_length is not None: - dataset = dataset.map( + dataset = dataset.traj_map( partial(_subsample, subsample_length=subsample_length), num_parallel_calls ) - dataset = dataset.map( + dataset = dataset.traj_map( partial( _chunk_act_obs, window_size=window_size, additional_action_window_size=additional_action_window_size, - action_encoding=action_encoding, ), num_parallel_calls, ) @@ -277,7 +276,9 @@ def move_language_instruction_to_tasks(traj): return dataset -def get_frame_transforms( +def apply_frame_transforms( + dataset: dl.DLataset, + *, train: bool, image_augment_kwargs: Union[Optional[dict], Sequence[Optional[dict]]] = None, resize_size: Union[ @@ -288,61 +289,66 @@ def get_frame_transforms( ] = None, task_augment_strategy: Optional[str] = None, task_augment_kwargs: dict = {}, -) -> List[Callable[[dict], dict]]: - """ - Returns a list of functions to be applied to each frame. These transforms are usually - more CPU-intensive, (e.g. decoding or resizing images). + num_parallel_calls: int = tf.data.AUTOTUNE, +) -> dl.DLataset: + """Applies common transforms that happen at a frame level. These transforms are usually more + CPU-intensive, (e.g. decoding or resizing images). Args: train (bool): Whether the dataset is for training (affects image augmentation). - image_augment_kwargs (dict or Sequence[dict]): Keyword arguments to pass to the image - augmentation function. See `dlimp.transforms.augment_image` for documentation. If a list - of dicts is provided, then the ith entry will be used for "image_i" (order determined by - "image_obs_keys"). A value of None or a None list entry will skip image augmentation for - the corresponding image(s). - resize_size (Tuple[int, int] or Sequence[Tuple[int, int]]): If provided, images will be - resized to this size. If a list of tuples is provided, then the ith entry will be used - for "image_i" and "depth_i" (order determined by "image_obs_keys" and "depth_obs_keys", - respectively). A value of None or a None list entry will skip resizing for the + dataset (dl.DLataset): The dataset to transform. + image_augment_kwargs (dict|Sequence[dict]): Keyword arguments to pass to the image augmentation + function. See `dlimp.transforms.augment_image` for documentation of these kwargs. If a list of dicts + is provided, then the ith entry will be used for "image_i" (order determined by `image_obs_keys` in + `make_dataset_from_rlds`). A None list entry will skip image augmentation for the corresponding + image(s). + resize_size (Tuple[int, int]|Sequence[Tuple[int, int]]): If provided, images will be + resized to this size. If a list of tuples is provided, then the ith entry will be used for + "image_i" and "depth_i" (order determined by `image_obs_keys` and `depth_obs_keys`, respectively, + in `make_dataset_from_rlds`). A value of None or a None list entry will skip resizing for the corresponding image(s). - depth_resize_size (Tuple[int, int] or Sequence[Tuple[int, int]]): Same as resize_size, but - for depth images. - task_augmentation_strategy (Optional[str], optional): The task augmentation strategy to use, or None for no task + depth_resize_size (Tuple[int, int]|Sequence[Tuple[int, int]]): Same as resize_size, but for depth + images. + task_augmentation_strategy (str, optional): The task augmentation strategy to use, or None for no task augmentation. See `task_augmentation.py`. - task_augmentation_kwargs (dict, optional): Additional keyword arguments to pass to the task augmentation function. + task_augmentation_kwargs (dict, optional): Additional keyword arguments to pass to the task + augmentation function. + num_parallel_calls (int): number of parallel calls for frame_map operations. Default to AUTOTUNE. """ - # convenience wrapper that takes a function that operates on a non-chunked "observation" dict - # and applies it to the chunked "observation" dict as well as the "tasks" dict + # convenience wrapper that takes a function that operates on a non-chunked "observation" dict and applies + # it to the chunked "observation" dict as well as the non-chunked "task" dict def apply_obs_transform(fn: Callable[[dict], dict], frame): - # tasks is not chunked -- apply fn directly - frame["tasks"] = fn(frame["tasks"]) + # task is not chunked -- apply fn directly + frame["task"] = fn(frame["task"]) # observation is chunked -- apply fn along first axis frame["observation"] = dl.vmap(fn)(frame["observation"]) return frame - transforms = [] - if train and task_augment_strategy is not None: # perform task augmentation (e.g., dropping keys) - transforms.append( + dataset = dataset.frame_map( partial( getattr(task_augmentation, task_augment_strategy), **task_augment_kwargs, ), + num_parallel_calls, ) # decode images (and depth images) - transforms.append(partial(apply_obs_transform, _decode_images)) + dataset = dataset.frame_map( + partial(apply_obs_transform, _decode_images), num_parallel_calls + ) # resize images (and depth images) - transforms.append( + dataset = dataset.frame_map( partial( apply_obs_transform, partial( _resize, resize_size=resize_size, depth_resize_size=depth_resize_size ), - ) + ), + num_parallel_calls, ) if train: @@ -352,63 +358,71 @@ def aug(frame): aug_fn = partial(_augment, seed=seed, augment_kwargs=image_augment_kwargs) return apply_obs_transform(aug_fn, frame) - transforms.append(aug) + dataset = dataset.frame_map(aug, num_parallel_calls) - return transforms + return dataset def make_dataset_from_rlds( name: str, data_dir: str, train: bool, + standardize_fn: Optional[Callable[[dict], dict]] = None, shuffle: bool = True, image_obs_keys: Union[str, Sequence[str]] = (), depth_obs_keys: Union[str, Sequence[str]] = (), state_obs_keys: Union[str, Sequence[str]] = (), - state_encoding: StateEncoding = StateEncoding.NONE, - action_encoding: ActionEncoding = ActionEncoding.EEF_POS, - action_proprio_normalization_type: Optional[str] = None, + action_proprio_normalization_type: NormalizationType = NormalizationType.NORMAL, dataset_statistics: Optional[Union[dict, str]] = None, num_parallel_reads: int = tf.data.AUTOTUNE, num_parallel_calls: int = tf.data.AUTOTUNE, -) -> Tuple[dl.DLataset, Optional[dict]]: +) -> Tuple[dl.DLataset, dict]: """This function is responsible for loading a specific RLDS dataset from storage and getting it into a - standardized format (see below). Yields a dataset of trajectories. Does not include CPU-intensive operations. + standardized format. Yields a dataset of trajectories. Does not include CPU-intensive operations. + + If `standardize_fn` is provided, it will be applied to each trajectory. This function should get the + trajectory into a standard format, which includes the keys "observation", "action", "is_terminal", and + "is_last". "observation" should be a dictionary containing some number of additional keys, which will be + extracted into an even more standardized numbered format according to the "*_obs_keys" arguments. + + For example, if the "observation" dict has the keys "image_workspace" and "image_wrist" after + `standardize_fn`, and `image_obs_keys=("image_workspace", None, "image_wrist")`, then the resulting + dataset will have an "observation" dict containing the keys "image_0", "image_1", and "image_2", where + "image_0" corresponds to "image_workspace", "image_1" is a padding image, and "image_2" corresponds to + "image_wrist". Args: name (str): The name of the RLDS dataset (usually "name" or "name:version"). data_dir (str): The path to the data directory. train (bool): Whether to use the training or validation set. - shuffle (bool, optional): Whether to shuffle the order of tfrecords. - image_obs_keys (str, List[str], optional): List of image observation keys to be decoded. Mapped to "image_XXX". - Inserts padding image for each None key. - depth_obs_keys (str, List[str], optional): List of depth observation keys to be decoded. Mapped to "depth_XXX". - Inserts padding image for each None key. - state_obs_keys (str, List[str], optional): List of low-dim observation keys to be decoded. - Get concatenated and mapped to "proprio". Inserts 1d padding for each None key. - state_encoding (StateEncoding): type of state encoding used, e.g. joint angles vs EEF pose. - action_encoding (ActionEncoding): type of action encoding used, e.g. joint delta vs EEF delta. - action_proprio_normalization_type (Optional[str], optional): The type of normalization to perform on the action, + shuffle (bool, optional): Whether to shuffle the file read order (does NOT fully shuffle directories, + since one file usually contains many trajectories!). + image_obs_keys (str|Sequence[str], optional): List of keys to be extracted from the "observation" + dict and mapped to "image_{i}". Inserts padding (an empty string) for each None entry. + depth_obs_keys (str|Sequence[str], optional): List of keys to be extracted from the "observation" + dict and mapped to "depth_{i}". Inserts padding (an empty string) for each None entry. + state_obs_keys (str|Sequence[str], optional): List of 1-dimensional proprioception keys to be + extracted from the "observation" dict, concatenated, and mapped to "proprio". Inserts 1 element of + padding (zero) for each None entry. + action_proprio_normalization_type (str, optional): The type of normalization to perform on the action, proprio, or both. Can be "normal" (mean 0, std 1) or "bounds" (normalized to [-1, 1]). - dataset_statistics: (dict|str, optional): dict (or path to JSON file) that contains dataset - statistics for normalization. If `action_proprio_normalization_type` is "normal", this - should contain "mean" and "std" keys. If `action_proprio_normalization_type` is "bounds", - this should contain "min" and "max" keys. May also provide "num_transitions" and - "num_trajectories" keys for downstream usage (e.g., for `make_interleaved_dataset`). If - not provided, the statistics will be computed on the fly based on the train split of the - dataset. - num_parallel_reads: number of parallel read workers. Default to AUTOTUNE. - num_parallel_calls: number of parallel calls for map operations. Default to AUTOTUNE. + dataset_statistics: (dict|str, optional): dict (or path to JSON file) that contains dataset statistics + for normalization. If `action_proprio_normalization_type` is "normal", this should contain "mean" and + "std" keys. If `action_proprio_normalization_type` is "bounds", this should contain "min" and "max" + keys. May also provide "num_transitions" and "num_trajectories" keys for downstream usage (e.g., for + `make_interleaved_dataset`). If not provided, the statistics will be computed on the fly. + num_parallel_reads (int): number of parallel read workers. Default to AUTOTUNE. + num_parallel_calls (int): number of parallel calls for traj_map operations. Default to AUTOTUNE. Returns: Dataset of trajectories where each step has the following fields: - observation: - - image_[0...N] # RGB image observations - - depth_[0...N] # depth image observations - - proprio # concatenated low-dim observations + - image_{0, 1, ..., N} # RGB image observations + - depth_{0, 1, ..., N} # depth image observations + - proprio # 1-dimensional array of proprioceptive observations - action # action vector - - language_instruction # language instruction string - is_last # boolean indicator, 1 on last step - is_terminal # boolean indicator, 1 on last step *if not timeout* + - language_instruction # string language instruction (optional) """ builder = tfds.builder(name, data_dir=data_dir) if "val" not in builder.info.splits: @@ -420,110 +434,91 @@ def make_dataset_from_rlds( builder, split=split, shuffle=shuffle, num_parallel_reads=num_parallel_reads ) - image_obs_keys = ( - [image_obs_keys] if not isinstance(image_obs_keys, Sequence) else image_obs_keys - ) - depth_obs_keys = ( - [depth_obs_keys] if not isinstance(depth_obs_keys, Sequence) else depth_obs_keys - ) - state_obs_keys = ( - [state_obs_keys] if not isinstance(state_obs_keys, Sequence) else state_obs_keys - ) + if not isinstance(image_obs_keys, Sequence): + image_obs_keys = [image_obs_keys] + if not isinstance(depth_obs_keys, Sequence): + depth_obs_keys = [depth_obs_keys] + if not isinstance(state_obs_keys, Sequence): + state_obs_keys = [state_obs_keys] def restructure(traj): - # apply any dataset-specific transforms - rlds_transform = RLDS_TRAJECTORY_MAP_TRANSFORMS[name] - - # skip None transforms - if rlds_transform is not None: - traj = rlds_transform(traj) - - # remove unused keys - keep_keys = [ + standard_keys = { "observation", "action", - "language_instruction", "is_terminal", "is_last", - "_traj_index", - ] - traj = {k: v for k, v in traj.items() if k in keep_keys} + } + + # apply a standardization function, if provided + if standardize_fn is not None: + traj = standardize_fn(traj) - # extracts RGB images, depth images and proprio based on provided keys, pad for all None keys - orig_obs = traj.pop("observation") + if not all(k in traj for k in standard_keys): + raise ValueError( + f"Trajectory is missing keys: {standard_keys - set(traj.keys())}. " + "Did you write a `standardize_fn`?" + ) + + # filter out keys that are not needed + allowed_keys = standard_keys | {"language_instruction"} + traj = {k: v for k, v in traj.items() if k in allowed_keys} + + # extracts images, depth images and proprio from the "observation" dict traj_len = tf.shape(traj["action"])[0] - traj["observation"] = {} + old_obs = traj["observation"] + new_obs = {} for i, key in enumerate(image_obs_keys): if key is None: - # pad with empty string - traj["observation"][f"image_{i}"] = tf.repeat("", traj_len) + new_obs[f"image_{i}"] = tf.repeat("", traj_len) # padding else: - traj["observation"][f"image_{i}"] = orig_obs[key] + new_obs[f"image_{i}"] = old_obs[key] + for i, key in enumerate(depth_obs_keys): if key is None: - # pad with empty string - traj["observation"][f"depth_{i}"] = tf.repeat("", traj_len) + new_obs[f"depth_{i}"] = tf.repeat("", traj_len) # padding else: - traj["observation"][f"depth_{i}"] = orig_obs[key] - if state_obs_keys: - proprio = [] - for key in state_obs_keys: - if key is None: - # pad with zero - proprio.append(tf.zeros((traj_len, 1), dtype=tf.float32)) - else: - proprio.append(tf.cast(orig_obs[key], tf.float32)) - traj["observation"]["proprio"] = tf.concat(proprio, axis=-1) - # make sure state encoding has correct length - if state_encoding != StateEncoding.NONE: - assert traj["observation"]["proprio"].shape[ - -1 - ] == state_encoding_length(state_encoding), ( - f"State encoding {state_encoding} for dataset {name} expects {state_encoding_length(state_encoding)}-dim proprio" - f" but got {traj['observation']['proprio'].shape[-1]}." - ) + new_obs[f"depth_{i}"] = old_obs[key] - # make sure action encoding has correct length - assert traj["action"].shape[-1] == action_encoding_length(action_encoding), ( - f"Action encoding {action_encoding} for dataset {name} expects {action_encoding_length(action_encoding)}-dim actions" - f" but got {traj['action'].shape[-1]}." - ) - traj["action"] = tf.cast(traj["action"], tf.float32) - - # check that all other keys are present - for key in ["action", "language_instruction", "is_last", "is_terminal"]: - if key not in traj: - raise ValueError(f"Key {key} is missing from trajectory: {traj}") - - # add state and action encoding info - traj["observation"]["state_encoding"] = tf.repeat(state_encoding, traj_len)[ - ..., None - ] - traj["observation"]["action_encoding"] = tf.repeat(action_encoding, traj_len)[ - ..., None - ] + if state_obs_keys: + new_obs["proprio"] = tf.concat( + [ + tf.zeros((traj_len, 1), dtype=tf.float32) # padding + if key is None + else tf.cast(old_obs[key], tf.float32) + for key in state_obs_keys + ], + axis=1, + ) # add timestep info - traj["observation"]["timestep"] = tf.range(traj_len) + 1 + new_obs["timestep"] = tf.range(traj_len) + 1 + + traj["action"] = tf.cast(traj["action"], tf.float32) + traj["observation"] = new_obs # add name of dataset traj["dataset_name"] = tf.repeat(name, traj_len) return traj - dataset = dataset.map(restructure, num_parallel_calls) - + # load or compute dataset statistics if isinstance(dataset_statistics, str): with tf.io.gfile.GFile(dataset_statistics, "r") as f: dataset_statistics = json.load(f) elif dataset_statistics is None: # tries to load from cache, otherwise computes on the fly dataset_statistics = get_dataset_statistics( - builder, state_obs_keys, restructure, RLDS_TRAJECTORY_MAP_TRANSFORMS[name] + builder, + restructure, + hash_dependencies=( + str(state_obs_keys), + inspect.getsource(standardize_fn) if standardize_fn is not None else "", + ), ) dataset_statistics = tree_map(np.array, dataset_statistics) - dataset = dataset.map( + dataset = dataset.traj_map(restructure, num_parallel_calls) + dataset = dataset.traj_map( partial( normalize_action_and_proprio, metadata=dataset_statistics, @@ -540,7 +535,6 @@ def make_single_dataset( traj_transform_kwargs: dict, frame_transform_kwargs: dict, train: bool, - frame_transform_threads: int = tf.data.AUTOTUNE, ) -> dl.DLataset: """Creates a single dataset from kwargs. Returns a dataset of trajectories. @@ -549,13 +543,16 @@ def make_single_dataset( traj_transform_kwargs: kwargs passed to 'apply_trajectory_transforms'. frame_transform_kwargs: kwargs passed to 'get_frame_transforms'. train: whether this is a training or validation dataset. - frame_transform_threads: number of parallel calls for frame transforms. Default to AUTOTUNE. """ - dataset, dataset_statistics = make_dataset_from_rlds(**dataset_kwargs, train=train) + dataset, dataset_statistics = make_dataset_from_rlds( + **dataset_kwargs, + standardize_fn=RLDS_STANDARDIZATION_TRANSFORMS.get( + dataset_kwargs["name"], None + ), + train=train, + ) dataset = apply_trajectory_transforms(dataset, **traj_transform_kwargs, train=train) - - for fn in get_frame_transforms(**frame_transform_kwargs, train=train): - dataset = dataset.frame_map(fn, frame_transform_threads) + dataset = apply_frame_transforms(dataset, **frame_transform_kwargs, train=train) # this seems to reduce memory usage without affecting speed dataset = dataset.with_ram_budget(1) @@ -567,7 +564,7 @@ def make_single_dataset( def make_interleaved_dataset( *, - dataset_kwargs_list: List[dict], + dataset_kwargs_list: Sequence[dict], traj_transform_kwargs: dict, frame_transform_kwargs: dict, train: bool, @@ -577,7 +574,6 @@ def make_interleaved_dataset( batch_size: int, traj_transform_threads: Optional[int], traj_read_threads: Optional[int], - frame_transform_threads: int, ) -> dl.DLataset: """Creates an interleaved dataset from list of dataset kwargs. Returns a dataset of batched frames. @@ -588,18 +584,16 @@ def make_interleaved_dataset( frame_transform_kwargs: kwargs passed to 'get_frame_transforms'. train: whether this is a training or validation dataset. sample_weights: sampling weights for each dataset in list. If None, defaults to uniform. - balance_weights: if True, the sample weights are multiplied by the number of frames in - each dataset. This makes it so that, if all the sample weights are equal, one full iteration - through the interleaved dataset will correspond to one full iteration through each - individual dataset (only in expectation, since in practice the sampling is random). + balance_weights: if True, the sample weights are multiplied by the number of frames in each dataset. + This makes it so that, if all the sample weights are equal, one full iteration through the interleaved + dataset will correspond to one full iteration through each individual dataset (only in expectation, + since in practice the sampling is random). shuffle_buffer_size: size of the dataset shuffle buffer (in number of frames). batch_size: batch size. traj_transform_threads: total number of parallel calls for trajectory transforms, distributed across datasets according to their sampling weights. If None, defaults to AUTOTUNE for every dataset. traj_read_threads: total number of parallel read workers for trajectory transforms, distributed across datasets according to their sampling weights. If None, defaults to AUTOTUNE for every dataset. - frame_transform_threads: number of parallel calls for frame transforms, which happen after datasets - are interleaved. Default to AUTOTUNE. """ if not sample_weights: sample_weights = [1.0] * len(dataset_kwargs_list) @@ -609,7 +603,13 @@ def make_interleaved_dataset( dataset_sizes = [] all_dataset_statistics = [] for dataset_kwargs in dataset_kwargs_list: - _, dataset_statistics = make_dataset_from_rlds(**dataset_kwargs, train=train) + _, dataset_statistics = make_dataset_from_rlds( + **dataset_kwargs, + standardize_fn=RLDS_STANDARDIZATION_TRANSFORMS.get( + dataset_kwargs["name"], None + ), + train=train, + ) dataset_sizes.append(dataset_statistics["num_transitions"]) all_dataset_statistics.append(dataset_statistics) @@ -634,7 +634,6 @@ def make_interleaved_dataset( # construct datasets datasets = [] - action_encodings = [] for dataset_kwargs, dataset_statistics, threads, reads in zip( dataset_kwargs_list, all_dataset_statistics, @@ -643,6 +642,9 @@ def make_interleaved_dataset( ): dataset, _ = make_dataset_from_rlds( **dataset_kwargs, + standardize_fn=RLDS_STANDARDIZATION_TRANSFORMS.get( + dataset_kwargs["name"], None + ), train=train, num_parallel_calls=threads, num_parallel_reads=reads, @@ -654,25 +656,17 @@ def make_interleaved_dataset( num_parallel_calls=threads, train=train, ).flatten(num_parallel_calls=threads) - action_encodings.append( - dataset_kwargs.get("action_encoding", ActionEncoding.EEF_POS) - ) datasets.append(dataset) - # TODO: support interleaving datasets with different action encodings - assert ( - len(set(action_encodings)) == 1 - ), f"Need action encodings of all datasets to be identical, currently used encodings: {action_encodings}." - # interleave at the transition level and then shuffle dataset: dl.DLataset = dl.DLataset.sample_from_datasets( datasets, sample_weights ).shuffle(shuffle_buffer_size) # apply frame transforms - for fn in get_frame_transforms(**frame_transform_kwargs, train=train): - dataset = dataset.map(fn, frame_transform_threads) + dataset = apply_frame_transforms(dataset, **frame_transform_kwargs, train=train) + # sequential batch (parallel batch seems to use much more memory) dataset = dataset.batch(batch_size) # this seems to reduce memory usage without affecting speed diff --git a/orca/data/dataset_transforms.py b/orca/data/dataset_transforms.py deleted file mode 100644 index 9b05dfc4..00000000 --- a/orca/data/dataset_transforms.py +++ /dev/null @@ -1,116 +0,0 @@ -"""Episode transforms for different RLDS datasets to canonical dataset definition.""" -from typing import Any, Dict - -import tensorflow as tf - -import orca.data.bridge.bridge_utils as bridge -from orca.data.oxe.oxe_dataset_transforms import * # noqa: F403 -from orca.data.utils.data_utils import binarize_gripper_actions - - -def r2_d2_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: - # every input feature is batched, ie has leading batch dimension - trajectory["action"] = tf.concat( - ( - trajectory["action_dict"]["cartesian_velocity"], - trajectory["action_dict"]["gripper_velocity"], - ), - axis=-1, - ) - return trajectory - - -def fmb_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: - # every input feature is batched, ie has leading batch dimension - trajectory["observation"]["state"] = tf.concat( - ( - trajectory["observation"]["state"], - trajectory["observation"]["gripper_state"][..., None], - ), - axis=-1, - ) - return trajectory - - -def bridge_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: - trajectory["action"] = tf.concat( - [ - trajectory["action"][:, :6], - binarize_gripper_actions(trajectory["action"][:, -1])[:, None], - ], - axis=1, - ) - trajectory = bridge.relabel_actions(trajectory) - trajectory["observation"]["EEF_state"] = trajectory["observation"]["state"][:, :6] - trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][ - :, -1: - ] - return trajectory - - -def aloha_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: - return trajectory - - -RLDS_TRAJECTORY_MAP_TRANSFORMS = { - "r2_d2": r2_d2_dataset_transform, - "r2_d2_pen_cmu_rgb": r2_d2_dataset_transform, - "r2_d2_play_cmu_rgb": r2_d2_dataset_transform, - "r2_d2_pen": r2_d2_dataset_transform, - "fmb_dataset": fmb_dataset_transform, - "bridge_dataset": bridge_dataset_transform, - "aloha_screwdriver_dataset": aloha_dataset_transform, - "aloha_sim_cube_scripted_dataset": aloha_dataset_transform, - # Open X-Embodiment Datasets - "fractal20220817_data": rt1_dataset_transform, - "kuka": kuka_dataset_transform, - "taco_play": taco_play_dataset_transform, - "jaco_play": jaco_play_dataset_transform, - "berkeley_cable_routing": berkeley_cable_routing_dataset_transform, - "roboturk": roboturk_dataset_transform, - "nyu_door_opening_surprising_effectiveness": nyu_door_opening_dataset_transform, - "viola": viola_dataset_transform, - "berkeley_autolab_ur5": berkeley_autolab_ur5_dataset_transform, - "toto": toto_dataset_transform, - "language_table": language_table_dataset_transform, - "columbia_cairlab_pusht_real": pusht_dataset_transform, - "stanford_kuka_multimodal_dataset_converted_externally_to_rlds": stanford_kuka_multimodal_dataset_transform, - "nyu_rot_dataset_converted_externally_to_rlds": nyu_rot_dataset_transform, - "stanford_hydra_dataset_converted_externally_to_rlds": stanford_hydra_dataset_transform, - "austin_buds_dataset_converted_externally_to_rlds": austin_buds_dataset_transform, - "nyu_franka_play_dataset_converted_externally_to_rlds": nyu_franka_play_dataset_transform, - "maniskill_dataset_converted_externally_to_rlds": maniskill_dataset_transform, - "furniture_bench_dataset_converted_externally_to_rlds": furniture_bench_dataset_transform, - "cmu_franka_exploration_dataset_converted_externally_to_rlds": cmu_franka_exploration_dataset_transform, - "ucsd_kitchen_dataset_converted_externally_to_rlds": ucsd_kitchen_dataset_transform, - "ucsd_pick_and_place_dataset_converted_externally_to_rlds": ucsd_pick_place_dataset_transform, - "austin_sailor_dataset_converted_externally_to_rlds": austin_sailor_dataset_transform, - "austin_sirius_dataset_converted_externally_to_rlds": austin_sirius_dataset_transform, - "bc_z": bc_z_dataset_transform, - "utokyo_pr2_opening_fridge_converted_externally_to_rlds": tokyo_pr2_opening_fridge_dataset_transform, - "utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds": tokyo_pr2_tabletop_manipulation_dataset_transform, - "utokyo_xarm_pick_and_place_converted_externally_to_rlds": utokyo_xarm_pick_place_dataset_transform, - "utokyo_xarm_bimanual_converted_externally_to_rlds": utokyo_xarm_bimanual_dataset_transform, - "robo_net": robo_net_dataset_transform, - "berkeley_mvp_converted_externally_to_rlds": berkeley_mvp_dataset_transform, - "berkeley_rpt_converted_externally_to_rlds": berkeley_rpt_dataset_transform, - "kaist_nonprehensile_converted_externally_to_rlds": kaist_nonprehensible_dataset_transform, - "stanford_mask_vit_converted_externally_to_rlds": stanford_mask_vit_dataset_transform, - "tokyo_u_lsmo_converted_externally_to_rlds": tokyo_lsmo_dataset_transform, - "dlr_sara_pour_converted_externally_to_rlds": dlr_sara_pour_dataset_transform, - "dlr_sara_grid_clamp_converted_externally_to_rlds": dlr_sara_grid_clamp_dataset_transform, - "dlr_edan_shared_control_converted_externally_to_rlds": dlr_edan_shared_control_dataset_transform, - "asu_table_top_converted_externally_to_rlds": asu_table_top_dataset_transform, - "stanford_robocook_converted_externally_to_rlds": robocook_dataset_transform, - "imperialcollege_sawyer_wrist_cam": imperial_wristcam_dataset_transform, - "iamlab_cmu_pickup_insert_converted_externally_to_rlds": iamlab_pick_insert_dataset_transform, - "uiuc_d3field": uiuc_d3field_dataset_transform, - "utaustin_mutex": utaustin_mutex_dataset_transform, - "berkeley_fanuc_manipulation": berkeley_fanuc_dataset_transform, - "cmu_playing_with_food": cmu_playing_with_food_dataset_transform, - "cmu_play_fusion": playfusion_dataset_transform, - "cmu_stretch": cmu_stretch_dataset_transform, - "berkeley_gnm_recon": gnm_dataset_transform, - "berkeley_gnm_cory_hall": gnm_dataset_transform, - "berkeley_gnm_sac_son": gnm_dataset_transform, -} diff --git a/orca/data/oxe/oxe_dataset_configs.py b/orca/data/oxe/oxe_dataset_configs.py index a09b8ec7..37b64b60 100755 --- a/orca/data/oxe/oxe_dataset_configs.py +++ b/orca/data/oxe/oxe_dataset_configs.py @@ -14,7 +14,26 @@ state_encoding: Type of state encoding used -- see above action_encoding: Type of action encoding used, e.g. EEF position vs joint position control """ -from orca.data.utils.data_utils import ActionEncoding, StateEncoding +from enum import IntEnum + + +class StateEncoding(IntEnum): + """Defines supported proprio state encoding schemes for different datasets.""" + + NONE = -1 # no state provided + POS_EULER = 1 # EEF XYZ + roll-pitch-yaw + 1 x pad + gripper open/close + POS_QUAT = 2 # EEF XYZ + quaternion + gripper open/close + JOINT = 3 # 7 x joint angles (padding added if fewer) + gripper open/close + JOINT_BIMANUAL = 4 # 2 x [6 x joint angles + gripper open/close] + + +class ActionEncoding(IntEnum): + """Defines supported action encoding schemes for different datasets.""" + + EEF_POS = 1 # EEF delta XYZ + roll-pitch-yaw + gripper open/close + JOINT_POS = 2 # 7 x joint delta position + gripper open/close + JOINT_POS_BIMANUAL = 3 # 2 x [6 x joint pos + gripper] + OXE_DATASET_KWARGS = { "fractal20220817_data": { diff --git a/orca/data/oxe/oxe_dataset_mixes.py b/orca/data/oxe/oxe_dataset_mixes.py index 7dde499f..fd2e0fea 100755 --- a/orca/data/oxe/oxe_dataset_mixes.py +++ b/orca/data/oxe/oxe_dataset_mixes.py @@ -7,7 +7,6 @@ import tqdm from orca.data.oxe import oxe_dataset_configs -from orca.data.utils.data_utils import ActionEncoding BRIDGE_MIX = [ ("bridge_dataset", 1.0), @@ -180,7 +179,10 @@ def make_oxe_dataset_kwargs_and_weights( data_kwargs_list, weights = [], [] for dataset, weight in data_mix: dataset_kwargs = copy.deepcopy(oxe_dataset_configs.OXE_DATASET_KWARGS[dataset]) - if dataset_kwargs["action_encoding"] is not ActionEncoding.EEF_POS: + if ( + dataset_kwargs["action_encoding"] + is not oxe_dataset_configs.ActionEncoding.EEF_POS + ): print( f"Skipping {dataset} since only EEF pose delta action encoding " f"is supported." @@ -212,6 +214,9 @@ def make_oxe_dataset_kwargs_and_weights( if not load_proprio: dataset_kwargs.pop("state_obs_keys") + del dataset_kwargs["state_encoding"] + del dataset_kwargs["action_encoding"] + # add dataset to list data_kwargs_list.append( {"name": dataset, "data_dir": data_dir, **dataset_kwargs} @@ -219,39 +224,3 @@ def make_oxe_dataset_kwargs_and_weights( weights.append(weight) return data_kwargs_list, weights - - -if __name__ == "__main__": - from orca.data.dataset import make_interleaved_dataset - - base_data_config = dict( - window_size=4, - image_augment_kwargs=dict( - random_resized_crop=dict(scale=[0.8, 1.0], ratio=[0.9, 1.1]), - random_brightness=[0.2], - random_contrast=[0.8, 1.2], - random_saturation=[0.8, 1.2], - random_hue=[0.1], - augment_order=[ - "random_resized_crop", - "random_brightness", - "random_contrast", - "random_saturation", - "random_hue", - ], - ), - goal_relabeling_strategy="uniform", - action_proprio_normalization_type="normal", - resize_size=(256, 256), - ) - data_kwargs_list, weights = make_oxe_dataset_kwargs_and_weights( - data_mix=RT_X_MIX, - data_dir="gs://rail-orca-central1", - balance_sampling_ratios=True, - n_third_person_cameras=1, - load_depth=False, - ) - ds = make_interleaved_dataset( - base_data_config, data_kwargs_list, train=True, sample_weights=weights - ) - print(ds.element_spec) diff --git a/orca/data/oxe/oxe_dataset_transforms.py b/orca/data/oxe/oxe_dataset_transforms.py index a88d2ac8..82aef780 100755 --- a/orca/data/oxe/oxe_dataset_transforms.py +++ b/orca/data/oxe/oxe_dataset_transforms.py @@ -12,11 +12,9 @@ } """ -import math from typing import Any, Dict import tensorflow as tf -import tensorflow_datasets as tfds from orca.data.utils.data_utils import ( binarize_gripper_actions, diff --git a/orca/data/standardization_transforms.py b/orca/data/standardization_transforms.py new file mode 100644 index 00000000..d6c280a4 --- /dev/null +++ b/orca/data/standardization_transforms.py @@ -0,0 +1,116 @@ +"""Episode transforms for different RLDS datasets to canonical dataset definition.""" +from typing import Any, Dict + +import tensorflow as tf + +import orca.data.bridge.bridge_utils as bridge +from orca.data.oxe import oxe_dataset_transforms as ox +from orca.data.utils.data_utils import binarize_gripper_actions + + +def r2_d2_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: + # every input feature is batched, ie has leading batch dimension + trajectory["action"] = tf.concat( + ( + trajectory["action_dict"]["cartesian_velocity"], + trajectory["action_dict"]["gripper_velocity"], + ), + axis=-1, + ) + return trajectory + + +def fmb_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: + # every input feature is batched, ie has leading batch dimension + trajectory["observation"]["state"] = tf.concat( + ( + trajectory["observation"]["state"], + trajectory["observation"]["gripper_state"][..., None], + ), + axis=-1, + ) + return trajectory + + +def bridge_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: + trajectory["action"] = tf.concat( + [ + trajectory["action"][:, :6], + binarize_gripper_actions(trajectory["action"][:, -1])[:, None], + ], + axis=1, + ) + trajectory = bridge.relabel_actions(trajectory) + trajectory["observation"]["EEF_state"] = trajectory["observation"]["state"][:, :6] + trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][ + :, -1: + ] + return trajectory + + +def aloha_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: + return trajectory + + +RLDS_STANDARDIZATION_TRANSFORMS = { + "r2_d2": r2_d2_dataset_transform, + "r2_d2_pen_cmu_rgb": r2_d2_dataset_transform, + "r2_d2_play_cmu_rgb": r2_d2_dataset_transform, + "r2_d2_pen": r2_d2_dataset_transform, + "fmb_dataset": fmb_dataset_transform, + "bridge_dataset": bridge_dataset_transform, + "aloha_screwdriver_dataset": aloha_dataset_transform, + "aloha_sim_cube_scripted_dataset": aloha_dataset_transform, + # Open X-Embodiment Datasets + "fractal20220817_data": ox.rt1_dataset_transform, + "kuka": ox.kuka_dataset_transform, + "taco_play": ox.taco_play_dataset_transform, + "jaco_play": ox.jaco_play_dataset_transform, + "berkeley_cable_routing": ox.berkeley_cable_routing_dataset_transform, + "roboturk": ox.roboturk_dataset_transform, + "nyu_door_opening_surprising_effectiveness": ox.nyu_door_opening_dataset_transform, + "viola": ox.viola_dataset_transform, + "berkeley_autolab_ur5": ox.berkeley_autolab_ur5_dataset_transform, + "toto": ox.toto_dataset_transform, + "language_table": ox.language_table_dataset_transform, + "columbia_cairlab_pusht_real": ox.pusht_dataset_transform, + "stanford_kuka_multimodal_dataset_converted_externally_to_rlds": ox.stanford_kuka_multimodal_dataset_transform, + "nyu_rot_dataset_converted_externally_to_rlds": ox.nyu_rot_dataset_transform, + "stanford_hydra_dataset_converted_externally_to_rlds": ox.stanford_hydra_dataset_transform, + "austin_buds_dataset_converted_externally_to_rlds": ox.austin_buds_dataset_transform, + "nyu_franka_play_dataset_converted_externally_to_rlds": ox.nyu_franka_play_dataset_transform, + "maniskill_dataset_converted_externally_to_rlds": ox.maniskill_dataset_transform, + "furniture_bench_dataset_converted_externally_to_rlds": ox.furniture_bench_dataset_transform, + "cmu_franka_exploration_dataset_converted_externally_to_rlds": ox.cmu_franka_exploration_dataset_transform, + "ucsd_kitchen_dataset_converted_externally_to_rlds": ox.ucsd_kitchen_dataset_transform, + "ucsd_pick_and_place_dataset_converted_externally_to_rlds": ox.ucsd_pick_place_dataset_transform, + "austin_sailor_dataset_converted_externally_to_rlds": ox.austin_sailor_dataset_transform, + "austin_sirius_dataset_converted_externally_to_rlds": ox.austin_sirius_dataset_transform, + "bc_z": ox.bc_z_dataset_transform, + "utokyo_pr2_opening_fridge_converted_externally_to_rlds": ox.tokyo_pr2_opening_fridge_dataset_transform, + "utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds": ox.tokyo_pr2_tabletop_manipulation_dataset_transform, + "utokyo_xarm_pick_and_place_converted_externally_to_rlds": ox.utokyo_xarm_pick_place_dataset_transform, + "utokyo_xarm_bimanual_converted_externally_to_rlds": ox.utokyo_xarm_bimanual_dataset_transform, + "robo_net": ox.robo_net_dataset_transform, + "berkeley_mvp_converted_externally_to_rlds": ox.berkeley_mvp_dataset_transform, + "berkeley_rpt_converted_externally_to_rlds": ox.berkeley_rpt_dataset_transform, + "kaist_nonprehensile_converted_externally_to_rlds": ox.kaist_nonprehensible_dataset_transform, + "stanford_mask_vit_converted_externally_to_rlds": ox.stanford_mask_vit_dataset_transform, + "tokyo_u_lsmo_converted_externally_to_rlds": ox.tokyo_lsmo_dataset_transform, + "dlr_sara_pour_converted_externally_to_rlds": ox.dlr_sara_pour_dataset_transform, + "dlr_sara_grid_clamp_converted_externally_to_rlds": ox.dlr_sara_grid_clamp_dataset_transform, + "dlr_edan_shared_control_converted_externally_to_rlds": ox.dlr_edan_shared_control_dataset_transform, + "asu_table_top_converted_externally_to_rlds": ox.asu_table_top_dataset_transform, + "stanford_robocook_converted_externally_to_rlds": ox.robocook_dataset_transform, + "imperialcollege_sawyer_wrist_cam": ox.imperial_wristcam_dataset_transform, + "iamlab_cmu_pickup_insert_converted_externally_to_rlds": ox.iamlab_pick_insert_dataset_transform, + "uiuc_d3field": ox.uiuc_d3field_dataset_transform, + "utaustin_mutex": ox.utaustin_mutex_dataset_transform, + "berkeley_fanuc_manipulation": ox.berkeley_fanuc_dataset_transform, + "cmu_playing_with_food": ox.cmu_playing_with_food_dataset_transform, + "cmu_play_fusion": ox.playfusion_dataset_transform, + "cmu_stretch": ox.cmu_stretch_dataset_transform, + "berkeley_gnm_recon": ox.gnm_dataset_transform, + "berkeley_gnm_cory_hall": ox.gnm_dataset_transform, + "berkeley_gnm_sac_son": ox.gnm_dataset_transform, +} diff --git a/orca/data/utils/data_utils.py b/orca/data/utils/data_utils.py index 6a5f1c2a..e5b12cec 100644 --- a/orca/data/utils/data_utils.py +++ b/orca/data/utils/data_utils.py @@ -1,10 +1,10 @@ -from enum import IntEnum +from enum import Enum import hashlib import inspect import json import logging import os -from typing import Any, Callable, Dict, List +from typing import Any, Callable, Dict, List, Tuple import dlimp as dl import numpy as np @@ -20,74 +20,30 @@ def tree_map(fn: Callable, tree: dict) -> dict: } -class StateEncoding(IntEnum): - """Defines supported proprio state encoding schemes for different datasets.""" +class NormalizationType(str, Enum): + """Defines supported normalization schemes for action and proprio.""" - NONE = -1 # no state provided - POS_EULER = 1 # EEF XYZ + roll-pitch-yaw + 1 x pad + gripper open/close - POS_QUAT = 2 # EEF XYZ + quaternion + gripper open/close - JOINT = 3 # 7 x joint angles (padding added if fewer) + gripper open/close - JOINT_BIMANUAL = 4 # 2 x [6 x joint angles + gripper open/close] + NORMAL = "normal" # normalize to mean 0, std 1 + BOUNDS = "bounds" # normalize to [-1, 1] -class ActionEncoding(IntEnum): - """Defines supported action encoding schemes for different datasets.""" - - EEF_POS = 1 # EEF delta XYZ + roll-pitch-yaw + gripper open/close - JOINT_POS = 2 # 7 x joint delta position + gripper open/close - JOINT_POS_BIMANUAL = 3 # 2 x [6 x joint pos + gripper] - - -def state_encoding_length(state_encoding): - if state_encoding == StateEncoding.NONE: - return 0 - # TODO: remove hack that POS_EULER pads 0 to match length - elif state_encoding in [ - StateEncoding.POS_EULER, - StateEncoding.POS_QUAT, - StateEncoding.JOINT, - ]: - return 8 - elif state_encoding in [StateEncoding.JOINT_BIMANUAL]: - return 14 - else: - raise ValueError(f"State encoding {state_encoding} not supported.") - - -def action_encoding_length(action_encoding): - if action_encoding in [ActionEncoding.EEF_POS]: - return 7 - elif action_encoding in [ActionEncoding.JOINT_POS]: - return 8 - elif action_encoding in [ActionEncoding.JOINT_POS_BIMANUAL]: - return 14 +def to_padding(tensor: tf.Tensor) -> tf.Tensor: + if tf.debugging.is_numeric_tensor(tensor): + return tf.zeros_like(tensor) + elif tensor.dtype == tf.string: + return tf.fill(tf.shape(tensor), "") else: - raise ValueError(f"Action encoding {action_encoding} not supported.") + raise ValueError(f"Cannot generate padding for tensor of type {tensor.dtype}.") -def make_zero_actions(action, action_encoding): +def make_neutral_actions( + action: tf.Tensor, absolute_action_mask: tf.Tensor +) -> tf.Tensor: + """Returns "neutral" actions, meaning relative actions are zeroed and absolute actions are retrained. + `absolute_action_mask` should be a 1D boolean mask that indicates which action dimensions are absolute. """ - Returns neutral action for action encoding, matches shape of input action. - Zero-action 0s out all relative actions and retains value of absolute actions like gripper open/close. - """ - assert action.shape[-1] == action_encoding_length(action_encoding), ( - f"For action encoding {action_encoding} expected {action_encoding_length(action_encoding)}-dim action," - f" but got {action.shape[-1]}-dim action." - ) - if action_encoding == ActionEncoding.EEF_POS: - is_absolute_action = tf.range(action.shape[-1]) >= 6 - elif action_encoding == ActionEncoding.JOINT_POS: - is_absolute_action = tf.range(action.shape[-1]) >= 7 - elif action_encoding == ActionEncoding.JOINT_POS_BIMANUAL: - is_absolute_action = tf.math.logical_or( - tf.range(action.shape[-1]) == 6, - tf.range(action.shape[-1]) == 13, - ) - else: - raise ValueError(f"Action encoding {action_encoding} not supported.") - return tf.where( - is_absolute_action[None, None, :], + absolute_action_mask[(None,) * (action.ndim - 1)], action, tf.zeros_like(action), ) @@ -112,27 +68,22 @@ def pprint_data_mixture( def get_dataset_statistics( builder: DatasetBuilder, - state_obs_keys: List[str], - restructure_fn: Callable, - transform_fn: Callable, + restructure_fn: Callable[[dict], dict], + hash_dependencies: Tuple[str, ...], ) -> dict: - """Either computes the statistics of a dataset or loads them from a cache file if this function - has been called before with the same arguments. Currently, the statistics include the - min/max/mean/std of the actions and proprio as well as the number of transitions and - trajectories in the dataset. + """Either computes the statistics of a dataset or loads them from a cache file if this function has been + called before with the same arguments. Currently, the statistics include the min/max/mean/std of the + actions and proprio as well as the number of transitions and trajectories in the dataset. """ - # compute a hash of the dataset info, state observation keys, and transform function - # to determine the name of the cache file - data_info_hash = hashlib.sha256( - ( - str(builder.info) - + str(state_obs_keys) - + str(inspect.getsource(restructure_fn)) - + str(inspect.getsource(transform_fn)) - ).encode("utf-8") + # compute a hash of the dataset info, restructure function source code, and any additional dependencies + unique_hash = hashlib.sha256( + "".join( + (str(builder.info), inspect.getsource(restructure_fn), *hash_dependencies) + ).encode(), + usedforsecurity=False, ).hexdigest() path = tf.io.gfile.join( - builder.info.data_dir, f"dataset_statistics_{data_info_hash}.json" + builder.info.data_dir, f"dataset_statistics_{unique_hash}.json" ) # fallback local path for when data_dir is not writable local_path = os.path.expanduser( @@ -141,7 +92,7 @@ def get_dataset_statistics( ".cache", "orca", builder.name, - f"dataset_statistics_{data_info_hash}.json", + f"dataset_statistics_{unique_hash}.json", ) ) @@ -158,19 +109,15 @@ def get_dataset_statistics( metadata = json.load(f) return metadata - if "val" not in builder.info.splits: - split = "train[:95%]" - expected_trajs = int(builder.info.splits["train"].num_examples * 0.95) - else: - split = "train" - expected_trajs = builder.info.splits["train"].num_examples dataset = ( - dl.DLataset.from_rlds(builder, split=split, shuffle=False) - .map(restructure_fn) - .map( + dl.DLataset.from_rlds(builder, split="train", shuffle=False) + .traj_map(restructure_fn) + .traj_map( lambda traj: { "action": traj["action"], - "proprio": traj["observation"]["proprio"], + "proprio": traj["observation"]["proprio"] + if "proprio" in traj["observation"] + else tf.zeros_like(traj["action"]), } ) ) @@ -184,7 +131,7 @@ def get_dataset_statistics( num_trajectories = 0 for traj in tqdm.tqdm( dataset.iterator(), - total=expected_trajs, + total=builder.info.splits["train"].num_examples, ): actions.append(traj["action"]) proprios.append(traj["proprio"]) @@ -224,13 +171,15 @@ def get_dataset_statistics( return metadata -def normalize_action_and_proprio(traj, metadata, normalization_type): +def normalize_action_and_proprio( + traj: dict, metadata: dict, normalization_type: NormalizationType +): # maps keys of `metadata` to corresponding keys in `traj` keys_to_normalize = { "action": "action", "proprio": "observation/proprio", } - if normalization_type == "normal": + if normalization_type == NormalizationType.NORMAL: # normalize to mean 0, std 1 for key, traj_key in keys_to_normalize.items(): traj = dl.transforms.selective_tree_map( @@ -241,7 +190,7 @@ def normalize_action_and_proprio(traj, metadata, normalization_type): ) return traj - if normalization_type == "bounds": + if normalization_type == NormalizationType.BOUNDS: # normalize to [-1, 1] for key, traj_key in keys_to_normalize.items(): traj = dl.transforms.selective_tree_map( diff --git a/orca/utils/train_callbacks.py b/orca/utils/train_callbacks.py index a9e1fbf8..50f17dee 100644 --- a/orca/utils/train_callbacks.py +++ b/orca/utils/train_callbacks.py @@ -47,9 +47,11 @@ def create_validation_dataset( **traj_transform_kwargs, "num_parallel_calls": 4, }, - frame_transform_kwargs=frame_transform_kwargs, + frame_transform_kwargs={ + **frame_transform_kwargs, + "num_parallel_calls": 16, + }, train=train, - frame_transform_threads=16, ) diff --git a/orca/utils/visualization_lib.py b/orca/utils/visualization_lib.py index f47788f6..0b7e6156 100644 --- a/orca/utils/visualization_lib.py +++ b/orca/utils/visualization_lib.py @@ -23,7 +23,6 @@ import tqdm import wandb -from orca.data.utils.data_utils import ActionEncoding from orca.utils.gym_wrappers import ( HistoryWrapper, RHCWrapper, @@ -203,12 +202,8 @@ def visualize_for_wandb( info = add_unnormalized_info(info, self.action_proprio_stats) info = add_manipulation_metrics(info) - if ( - int(traj["observation"]["action_encoding"][0, 0, 0]) - == ActionEncoding.EEF_POS - ): - plotly_fig = plot_trajectory_actions(**info) - visualizations[f"traj_{n}"] = plotly_fig + plotly_fig = plot_trajectory_actions(**info) + visualizations[f"traj_{n}"] = plotly_fig # plot qualitative action trajectory per dimension w/ and w/o action chunk visualizations[f"traj_{n}_mpl"] = plot_trajectory_overview_mpl( diff --git a/tests/debug_config.py b/tests/debug_config.py index 4d7d17a2..950d8e0c 100644 --- a/tests/debug_config.py +++ b/tests/debug_config.py @@ -40,9 +40,13 @@ def get_config(): "state_obs_keys": ["state"], }, ], + "frame_transform_kwargs": { + "resize_size": (128, 128), + "image_augment_kwargs": [None], + "num_parallel_calls": 4, + }, "traj_transform_threads": 1, # shared between all datasets "traj_read_threads": 1, # shared between all datasets - "frame_transform_threads": 4, # not shared between datasets "batch_size": 64, "sample_weights": None, "shuffle_buffer_size": 1000, From 39ff491f92155b539a19e102ac2b7c16ba764092 Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Thu, 7 Dec 2023 11:20:56 -0800 Subject: [PATCH 03/25] Remove unused imports and enforce --- .flake8 | 3 --- orca/data/dataset.py | 1 - orca/data/oxe/oxe_dataset_mixes.py | 4 ---- orca/data/utils/text_processing.py | 2 +- orca/model/components/vit_encoders.py | 1 - orca/utils/eval_utils.py | 5 +---- orca/utils/gym_wrappers.py | 1 - orca/utils/jax_utils.py | 9 +-------- orca/utils/run_eval.py | 7 +++---- orca/utils/visualization_lib.py | 4 +--- tests/debug_config.py | 2 -- 11 files changed, 7 insertions(+), 32 deletions(-) diff --git a/.flake8 b/.flake8 index c84eeb3f..e12debc7 100644 --- a/.flake8 +++ b/.flake8 @@ -6,11 +6,8 @@ ignore=W503, E203, E731, E722, - F401, F841, E402, E741, E501, C406, -per-file-ignores = - orca/data/dataset_transforms.py: F405 diff --git a/orca/data/dataset.py b/orca/data/dataset.py index fcf307d6..0d3630f5 100644 --- a/orca/data/dataset.py +++ b/orca/data/dataset.py @@ -15,7 +15,6 @@ from orca.data.utils.data_utils import ( allocate_threads, get_dataset_statistics, - make_neutral_actions, NormalizationType, normalize_action_and_proprio, pprint_data_mixture, diff --git a/orca/data/oxe/oxe_dataset_mixes.py b/orca/data/oxe/oxe_dataset_mixes.py index fd2e0fea..9a704ebd 100755 --- a/orca/data/oxe/oxe_dataset_mixes.py +++ b/orca/data/oxe/oxe_dataset_mixes.py @@ -2,10 +2,6 @@ import copy from typing import Any, Dict, List, Tuple -import numpy as np -import tensorflow_datasets as tfds -import tqdm - from orca.data.oxe import oxe_dataset_configs BRIDGE_MIX = [ diff --git a/orca/data/utils/text_processing.py b/orca/data/utils/text_processing.py index b9b00eb1..fff45eff 100644 --- a/orca/data/utils/text_processing.py +++ b/orca/data/utils/text_processing.py @@ -52,7 +52,7 @@ def encode(self, strings: Sequence[str]): class MuseEmbedding(TextProcessor): def __init__(self): import tensorflow_hub as hub # lazy import - import tensorflow_text # required for muse + import tensorflow_text # noqa: F401 self.muse_model = hub.load(MULTI_MODULE) diff --git a/orca/model/components/vit_encoders.py b/orca/model/components/vit_encoders.py index bc54b966..bc25e3ec 100644 --- a/orca/model/components/vit_encoders.py +++ b/orca/model/components/vit_encoders.py @@ -13,7 +13,6 @@ import jax.numpy as jnp from orca.model.components.film_conditioning_layer import FilmConditioning -from orca.model.components.transformer import Transformer T = TypeVar("T") diff --git a/orca/utils/eval_utils.py b/orca/utils/eval_utils.py index 14e90f49..734eeae1 100644 --- a/orca/utils/eval_utils.py +++ b/orca/utils/eval_utils.py @@ -1,14 +1,11 @@ from functools import partial import logging import os -from pathlib import Path, PurePath +from pathlib import Path import jax -import jax.numpy as jnp import tensorflow as tf -from orca.utils.pretrained_utils import PretrainedModel - def supply_rng(f, rng=jax.random.PRNGKey(0)): def wrapped(*args, **kwargs): diff --git a/orca/utils/gym_wrappers.py b/orca/utils/gym_wrappers.py index d13f9ae2..0b1d7cf3 100644 --- a/orca/utils/gym_wrappers.py +++ b/orca/utils/gym_wrappers.py @@ -2,7 +2,6 @@ import gym import gym.spaces -import jax import numpy as np diff --git a/orca/utils/jax_utils.py b/orca/utils/jax_utils.py index 62999716..8909b4af 100644 --- a/orca/utils/jax_utils.py +++ b/orca/utils/jax_utils.py @@ -1,18 +1,11 @@ -from copy import deepcopy -import io import logging import os -import pickle -from typing import Any, Callable, Optional, Sequence, Union +from typing import Any, Optional, Sequence import jax -from jax._src import xla_bridge as xb from jax.experimental import multihost_utils from jax.experimental.compilation_cache import compilation_cache import jax.numpy as jnp -from jax.stages import Compiled -from jaxlib.mlir import ir -from jaxlib.mlir.dialects import chlo, stablehlo import numpy as np diff --git a/orca/utils/run_eval.py b/orca/utils/run_eval.py index e0a2e43c..4541fd06 100644 --- a/orca/utils/run_eval.py +++ b/orca/utils/run_eval.py @@ -5,9 +5,9 @@ import json import os import time -from typing import Callable, Optional +from typing import Callable -from absl import app, flags, logging +from absl import flags, logging import click import cv2 import flax @@ -18,13 +18,12 @@ import numpy as np import tensorflow as tf -from orca.sim.widowx_sim_env import WidowXSimEnv from orca.utils.eval_utils import ( download_checkpoint_from_gcs, load_jaxrlm_checkpoint, supply_rng, ) -from orca.utils.gym_wrappers import HistoryWrapper, RHCWrapper, TemporalEnsembleWrapper +from orca.utils.gym_wrappers import HistoryWrapper, RHCWrapper from orca.utils.pretrained_utils import PretrainedModel np.set_printoptions(suppress=True) diff --git a/orca/utils/visualization_lib.py b/orca/utils/visualization_lib.py index 0b7e6156..0bfd7110 100644 --- a/orca/utils/visualization_lib.py +++ b/orca/utils/visualization_lib.py @@ -4,9 +4,8 @@ matplotlib.use("Agg") from dataclasses import dataclass -from functools import reduce import json -from typing import Any, Callable, Dict, Optional, Tuple, Union +from typing import Any, Dict, Optional, Union import dlimp as dl import flax @@ -17,7 +16,6 @@ import matplotlib.gridspec as gridspec import matplotlib.pyplot as plt import numpy as np -from PIL import Image import plotly.graph_objects as go import tensorflow as tf import tqdm diff --git a/tests/debug_config.py b/tests/debug_config.py index 950d8e0c..994878af 100644 --- a/tests/debug_config.py +++ b/tests/debug_config.py @@ -1,5 +1,3 @@ -from copy import deepcopy - from config import get_config as get_base_config from config import update_config From 481d7517451d22d568741c66256989f729422c79 Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Thu, 7 Dec 2023 12:22:38 -0800 Subject: [PATCH 04/25] Change camera view loading for OXE --- config.py | 3 +- orca/data/oxe/oxe_dataset_configs.py | 742 +++++++++------------------ orca/data/oxe/oxe_dataset_mixes.py | 58 +-- train.py | 16 +- 4 files changed, 282 insertions(+), 537 deletions(-) diff --git a/config.py b/config.py index 572e7894..14e3c1e3 100644 --- a/config.py +++ b/config.py @@ -126,8 +126,7 @@ def get_dataset_config(modality="multimodal", window_size=1): data_mix=placeholder(str), # for v4 TPUs: "gs://rail-orca-central2/resize_336_336" data_dir=placeholder(str), - n_third_person_cameras=1, - n_wrist_cameras=0, + load_camera_views=("primary", "wrist"), load_depth=False, ), # common_dataset_kwargs override specific kwargs from dataset_kwargs_list diff --git a/orca/data/oxe/oxe_dataset_configs.py b/orca/data/oxe/oxe_dataset_configs.py index 37b64b60..4d09ca32 100755 --- a/orca/data/oxe/oxe_dataset_configs.py +++ b/orca/data/oxe/oxe_dataset_configs.py @@ -2,11 +2,13 @@ Target configuration: image_obs_keys: - 2x external RGB - 1x wrist/ego RGB + primary: primary external RGB + secondary: secondary external RGB + wrist: wrist RGB depth_obs_keys: - 2x external depth - 1x wrist depth + primary: primary external depth + secondary: secondary external depth + wrist: wrist depth state_obs_keys: # 8-dim, changes based on used StateEncoding StateEncoding.POS_EULER: EEF XYZ + roll-pitch-yaw + 1 x pad + gripper open/close StateEncoding.POS_QUAT: EEF XYZ + quaternion + gripper open/close @@ -35,17 +37,17 @@ class ActionEncoding(IntEnum): JOINT_POS_BIMANUAL = 3 # 2 x [6 x joint pos + gripper] -OXE_DATASET_KWARGS = { +OXE_DATASET_CONFIGS = { "fractal20220817_data": { - "image_obs_keys": ["image", None, None], - "depth_obs_keys": [None, None, None], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, "state_obs_keys": ["base_pose_tool_reached", "gripper_closed"], "state_encoding": StateEncoding.POS_QUAT, "action_encoding": ActionEncoding.EEF_POS, }, "kuka": { - "image_obs_keys": ["image", None, None], - "depth_obs_keys": [None, None, None], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, "state_obs_keys": [ "clip_function_input/base_pose_tool_reached", "gripper_closed", @@ -54,321 +56,237 @@ class ActionEncoding(IntEnum): "action_encoding": ActionEncoding.EEF_POS, }, "bridge_dataset": { - "image_obs_keys": ["image_0", "image_1", None], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "EEF_state", - None, - "gripper_state", - ], + "image_obs_keys": {"primary": "image_0", "secondary": "image_1", "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["EEF_state", None, "gripper_state"], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "taco_play": { - "image_obs_keys": [ - "rgb_static", - None, - "rgb_gripper", - ], - "depth_obs_keys": [ - "depth_static", - None, - "depth_gripper", - ], + "image_obs_keys": { + "primary": "rgb_static", + "secondary": None, + "wrist": "rgb_gripper", + }, + "depth_obs_keys": { + "primary": "depth_static", + "secondary": None, + "wrist": "depth_gripper", + }, "state_obs_keys": ["state_eef", None, "state_gripper"], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "jaco_play": { - "image_obs_keys": [ - "image", - None, - "image_wrist", - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "state_eef", - None, - "state_gripper", - ], + "image_obs_keys": { + "primary": "image", + "secondary": None, + "wrist": "image_wrist", + }, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["state_eef", None, "state_gripper"], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "berkeley_cable_routing": { - "image_obs_keys": [ - "image", - "top_image", - "wrist45_image", - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "robot_state", - None, - ], + "image_obs_keys": { + "primary": "image", + "secondary": "top_image", + "wrist": "wrist45_image", + }, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["robot_state", None], "state_encoding": StateEncoding.JOINT, "action_encoding": ActionEncoding.EEF_POS, }, "roboturk": { - "image_obs_keys": [ - "front_rgb", - None, - None, - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [None] * 8, + "image_obs_keys": {"primary": "front_rgb", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": [None, None, None, None, None, None, None, None], "state_encoding": StateEncoding.NONE, "action_encoding": ActionEncoding.EEF_POS, }, "nyu_door_opening_surprising_effectiveness": { - "image_obs_keys": [ - None, - None, - "image", - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [None] * 8, + "image_obs_keys": {"primary": None, "secondary": None, "wrist": "image"}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": [None, None, None, None, None, None, None, None], "state_encoding": StateEncoding.NONE, "action_encoding": ActionEncoding.EEF_POS, }, "viola": { - "image_obs_keys": [ - "agentview_rgb", - None, - "eye_in_hand_rgb", - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "joint_states", - "gripper_states", - ], + "image_obs_keys": { + "primary": "agentview_rgb", + "secondary": None, + "wrist": "eye_in_hand_rgb", + }, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["joint_states", "gripper_states"], "state_encoding": StateEncoding.JOINT, "action_encoding": ActionEncoding.EEF_POS, }, "berkeley_autolab_ur5": { - "image_obs_keys": [ - "image", - None, - "hand_image", - ], - "depth_obs_keys": ["depth", None, None], - "state_obs_keys": [ - "state", - ], + "image_obs_keys": { + "primary": "image", + "secondary": None, + "wrist": "hand_image", + }, + "depth_obs_keys": {"primary": "depth", "secondary": None, "wrist": None}, + "state_obs_keys": ["state"], "state_encoding": StateEncoding.POS_QUAT, "action_encoding": ActionEncoding.EEF_POS, }, "toto": { - "image_obs_keys": [ - "image", - None, - None, - ], - "depth_obs_keys": [None, None, None], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, "state_obs_keys": ["state", None], "state_encoding": StateEncoding.JOINT, "action_encoding": ActionEncoding.EEF_POS, }, "language_table": { - "image_obs_keys": [ - "rgb", - None, - None, - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "effector_translation", - *([None] * 6), - ], + "image_obs_keys": {"primary": "rgb", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["effector_translation", None, None, None, None, None, None], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "columbia_cairlab_pusht_real": { - "image_obs_keys": [ - "image", - None, - "wrist_image", - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "robot_state", - *([None] * 6), - ], + "image_obs_keys": { + "primary": "image", + "secondary": None, + "wrist": "wrist_image", + }, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["robot_state", None, None, None, None, None, None], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "stanford_kuka_multimodal_dataset_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - None, - ], - "depth_obs_keys": ["depth_image", None, None], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": "depth_image", "secondary": None, "wrist": None}, "state_obs_keys": ["ee_position", "ee_orientation", None], "state_encoding": StateEncoding.POS_QUAT, "action_encoding": ActionEncoding.EEF_POS, }, "nyu_rot_dataset_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - None, - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "eef_state", - None, - "gripper_state", - ], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["eef_state", None, "gripper_state"], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "stanford_hydra_dataset_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - "wrist_image", - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "eef_state", - None, - "gripper_state", - ], + "image_obs_keys": { + "primary": "image", + "secondary": None, + "wrist": "wrist_image", + }, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["eef_state", None, "gripper_state"], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "austin_buds_dataset_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - "wrist_image", - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "state", - ], + "image_obs_keys": { + "primary": "image", + "secondary": None, + "wrist": "wrist_image", + }, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["state"], "state_encoding": StateEncoding.JOINT, "action_encoding": ActionEncoding.EEF_POS, }, "nyu_franka_play_dataset_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - "image_additional_view", - None, - ], - "depth_obs_keys": ["depth", "depth_additional_view", None], + "image_obs_keys": { + "primary": "image", + "secondary": "image_additional_view", + "wrist": None, + }, + "depth_obs_keys": { + "primary": "depth", + "secondary": "depth_additional_view", + "wrist": None, + }, "state_obs_keys": ["eef_state", None, None], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "maniskill_dataset_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - "wrist_image", - ], - "depth_obs_keys": [ - "depth", - None, - "wrist_depth", - ], - "state_obs_keys": [ - "tcp_pose", - "gripper_state", - ], + "image_obs_keys": { + "primary": "image", + "secondary": None, + "wrist": "wrist_image", + }, + "depth_obs_keys": { + "primary": "depth", + "secondary": None, + "wrist": "wrist_depth", + }, + "state_obs_keys": ["tcp_pose", "gripper_state"], "state_encoding": StateEncoding.POS_QUAT, "action_encoding": ActionEncoding.EEF_POS, }, "furniture_bench_dataset_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - "wrist_image", - ], - "depth_obs_keys": [ - None, - None, - None, - ], - "state_obs_keys": [ - "state", - ], + "image_obs_keys": { + "primary": "image", + "secondary": None, + "wrist": "wrist_image", + }, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["state"], "state_encoding": StateEncoding.POS_QUAT, "action_encoding": ActionEncoding.EEF_POS, }, "cmu_franka_exploration_dataset_converted_externally_to_rlds": { - "image_obs_keys": [ - "highres_image", - None, - None, - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [None] * 8, + "image_obs_keys": { + "primary": "highres_image", + "secondary": None, + "wrist": None, + }, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": [None, None, None, None, None, None, None, None], "state_encoding": StateEncoding.NONE, "action_encoding": ActionEncoding.EEF_POS, }, "ucsd_kitchen_dataset_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - None, - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "joint_state", - None, - ], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["joint_state", None], "state_encoding": StateEncoding.JOINT, "action_encoding": ActionEncoding.EEF_POS, }, "ucsd_pick_and_place_dataset_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - None, - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "eef_state", - None, - "gripper_state", - ], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["eef_state", None, "gripper_state"], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "austin_sailor_dataset_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - "wrist_image", - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "state", - ], + "image_obs_keys": { + "primary": "image", + "secondary": None, + "wrist": "wrist_image", + }, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["state"], "state_encoding": StateEncoding.POS_QUAT, "action_encoding": ActionEncoding.EEF_POS, }, "austin_sirius_dataset_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - "wrist_image", - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "state", - ], + "image_obs_keys": { + "primary": "image", + "secondary": None, + "wrist": "wrist_image", + }, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["state"], "state_encoding": StateEncoding.POS_QUAT, "action_encoding": ActionEncoding.EEF_POS, }, "bc_z": { - "image_obs_keys": [ - "image", - None, - None, - ], - "depth_obs_keys": [None, None, None], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, "state_obs_keys": [ "present/xyz", "present/axis_angle", @@ -379,374 +297,208 @@ class ActionEncoding(IntEnum): "action_encoding": ActionEncoding.EEF_POS, }, "utokyo_pr2_opening_fridge_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - None, - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "eef_state", - None, - "gripper_state", - ], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["eef_state", None, "gripper_state"], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - None, - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "eef_state", - None, - "gripper_state", - ], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["eef_state", None, "gripper_state"], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "utokyo_xarm_pick_and_place_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - "image2", - "hand_image", - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "end_effector_pose", - None, - None, - ], + "image_obs_keys": { + "primary": "image", + "secondary": "image2", + "wrist": "hand_image", + }, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["end_effector_pose", None, None], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "utokyo_xarm_bimanual_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - None, - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "pose_r", - None, - None, - ], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["pose_r", None, None], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "robo_net": { - "image_obs_keys": [ - "image", - "image1", - None, - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "eef_state", - None, - "gripper_state", - ], + "image_obs_keys": {"primary": "image", "secondary": "image1", "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["eef_state", None, "gripper_state"], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "berkeley_mvp_converted_externally_to_rlds": { - "image_obs_keys": [ - None, - None, - "hand_image", - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "pose", - "gripper", - ], + "image_obs_keys": {"primary": None, "secondary": None, "wrist": "hand_image"}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["pose", "gripper"], "state_encoding": StateEncoding.POS_QUAT, "action_encoding": ActionEncoding.JOINT_POS, }, "berkeley_rpt_converted_externally_to_rlds": { - "image_obs_keys": [ - None, - None, - "hand_image", - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "joint_pos", - "gripper", - ], + "image_obs_keys": {"primary": None, "secondary": None, "wrist": "hand_image"}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["joint_pos", "gripper"], "state_encoding": StateEncoding.JOINT, "action_encoding": ActionEncoding.JOINT_POS, }, "kaist_nonprehensile_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - None, - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "state", - None, - ], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["state", None], "state_encoding": StateEncoding.POS_QUAT, "action_encoding": ActionEncoding.EEF_POS, }, "stanford_mask_vit_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - None, - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "eef_state", - None, - "gripper_state", - ], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["eef_state", None, "gripper_state"], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "tokyo_u_lsmo_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - None, - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "eef_state", - None, - "gripper_state", - ], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["eef_state", None, "gripper_state"], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "dlr_sara_pour_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - None, - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "state", - None, - None, - ], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["state", None, None], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "dlr_sara_grid_clamp_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - None, - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "state", - None, - None, - ], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["state", None, None], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "dlr_edan_shared_control_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - None, - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "state", - None, - ], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["state", None], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "asu_table_top_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - None, - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "eef_state", - None, - "gripper_state", - ], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["eef_state", None, "gripper_state"], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "stanford_robocook_converted_externally_to_rlds": { - "image_obs_keys": [ - "image_1", - "image_2", - None, - ], - "depth_obs_keys": ["depth_1", "depth_2", None], - "state_obs_keys": [ - "eef_state", - None, - "gripper_state", - ], + "image_obs_keys": {"primary": "image_1", "secondary": "image_2", "wrist": None}, + "depth_obs_keys": {"primary": "depth_1", "secondary": "depth_2", "wrist": None}, + "state_obs_keys": ["eef_state", None, "gripper_state"], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "imperialcollege_sawyer_wrist_cam": { - "image_obs_keys": [ - "image", - None, - "wrist_image", - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - *([None] * 7), - "state", - ], + "image_obs_keys": { + "primary": "image", + "secondary": None, + "wrist": "wrist_image", + }, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": [None, None, None, None, None, None, None, "state"], "state_encoding": StateEncoding.NONE, "action_encoding": ActionEncoding.EEF_POS, }, "iamlab_cmu_pickup_insert_converted_externally_to_rlds": { - "image_obs_keys": [ - "image", - None, - "wrist_image", - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "joint_state", - "gripper_state", - ], + "image_obs_keys": { + "primary": "image", + "secondary": None, + "wrist": "wrist_image", + }, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["joint_state", "gripper_state"], "state_encoding": StateEncoding.JOINT, "action_encoding": ActionEncoding.EEF_POS, }, "uiuc_d3field": { - "image_obs_keys": [ - "image_1", - "image_2", - None, - ], - "depth_obs_keys": ["depth_1", "depth_2", None], - "state_obs_keys": [None] * 8, + "image_obs_keys": {"primary": "image_1", "secondary": "image_2", "wrist": None}, + "depth_obs_keys": {"primary": "depth_1", "secondary": "depth_2", "wrist": None}, + "state_obs_keys": [None, None, None, None, None, None, None, None], "state_encoding": StateEncoding.NONE, "action_encoding": ActionEncoding.EEF_POS, }, "utaustin_mutex": { - "image_obs_keys": [ - "image", - None, - "wrist_image", - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "state", - ], + "image_obs_keys": { + "primary": "image", + "secondary": None, + "wrist": "wrist_image", + }, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["state"], "state_encoding": StateEncoding.JOINT, "action_encoding": ActionEncoding.EEF_POS, }, "berkeley_fanuc_manipulation": { - "image_obs_keys": [ - "image", - None, - "wrist_image", - ], - "depth_obs_keys": [None, None, None], + "image_obs_keys": { + "primary": "image", + "secondary": None, + "wrist": "wrist_image", + }, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, "state_obs_keys": ["joint_state", None, "gripper_state"], "state_encoding": StateEncoding.JOINT, "action_encoding": ActionEncoding.EEF_POS, }, "cmu_playing_with_food": { - "image_obs_keys": [ - "image", - None, - "finger_vision_1", - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "state", - None, - None, - ], + "image_obs_keys": { + "primary": "image", + "secondary": None, + "wrist": "finger_vision_1", + }, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["state", None, None], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "cmu_play_fusion": { - "image_obs_keys": [ - "image", - None, - None, - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "state", - ], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["state"], "state_encoding": StateEncoding.JOINT, "action_encoding": ActionEncoding.EEF_POS, }, "cmu_stretch": { - "image_obs_keys": [ - "image", - None, - None, - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "eef_state", - None, - "gripper_state", - ], + "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["eef_state", None, "gripper_state"], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "berkeley_gnm_recon": { - "image_obs_keys": [ - None, - None, - "image", - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "state", - None, - None, - ], + "image_obs_keys": {"primary": None, "secondary": None, "wrist": "image"}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["state", None, None], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "berkeley_gnm_cory_hall": { - "image_obs_keys": [ - None, - None, - "image", - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "state", - None, - None, - ], + "image_obs_keys": {"primary": None, "secondary": None, "wrist": "image"}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["state", None, None], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, "berkeley_gnm_sac_son": { - "image_obs_keys": [ - None, - None, - "image", - ], - "depth_obs_keys": [None, None, None], - "state_obs_keys": [ - "state", - None, - None, - ], + "image_obs_keys": {"primary": None, "secondary": None, "wrist": "image"}, + "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None}, + "state_obs_keys": ["state", None, None], "state_encoding": StateEncoding.POS_EULER, "action_encoding": ActionEncoding.EEF_POS, }, diff --git a/orca/data/oxe/oxe_dataset_mixes.py b/orca/data/oxe/oxe_dataset_mixes.py index 9a704ebd..a25768f4 100755 --- a/orca/data/oxe/oxe_dataset_mixes.py +++ b/orca/data/oxe/oxe_dataset_mixes.py @@ -1,8 +1,9 @@ """Defines dataset mixtures and weights for the Open X-Embodiment Datasets.""" import copy -from typing import Any, Dict, List, Tuple +import logging +from typing import Any, Dict, List, Sequence, Tuple, Union -from orca.data.oxe import oxe_dataset_configs +from orca.data.oxe.oxe_dataset_configs import ActionEncoding, OXE_DATASET_CONFIGS BRIDGE_MIX = [ ("bridge_dataset", 1.0), @@ -131,7 +132,7 @@ ("berkeley_gnm_sac_son", 1.0), ] -mixes = { +OXE_NAMED_MIXES = { "bridge": BRIDGE_MIX, "rtx": RT_X_MIX, "rtx_franka": RT_X_MIX + OXE_FRANKA_MIX, @@ -140,11 +141,10 @@ def make_oxe_dataset_kwargs_and_weights( - data_mix: List[Tuple[str, float]], + data_mix: Union[str, Sequence[Tuple[str, float]]], data_dir: str, deduplicate: bool = True, - n_third_person_cameras: int = 1, - n_wrist_cameras: int = 0, + load_camera_views: Sequence[str] = ("primary",), load_depth: bool = True, load_proprio: bool = True, ) -> Tuple[Dict[str, Any], List[float]]: @@ -152,16 +152,20 @@ def make_oxe_dataset_kwargs_and_weights( Generates dataset kwargs for a given dataset mix from the Open X-Embodiment dataset. Args: - data_mix: List of (dataset name, sampling weight) tuples. + data_mix: List of (dataset name, sampling weight) tuples, or a string specifying a pre-defined mix to + load from `OXE_NAMED_MIXES` above. data_dir: Base data directory that gets registered in each dataset. deduplicate: If True, discards any duplicate dataset entries based on dataset name. - n_third_person_cameras: Number of RGB third person camera input streams to load. - n_wrist_cameras: Number of RGB wrist camera input streams to load. + load_camera_views: Which views to load from each dataset. See the top of `oxe_dataset_configs.py` + for available views. load_depth: If True, loads corresponding depth channels for each RGB channel. load_proprio: If True, loads proprioceptive information. Returns: Tuple of (dataset_kwargs_list, sampling weights). """ + if isinstance(data_mix, str): + data_mix = OXE_NAMED_MIXES[data_mix] + if deduplicate: filtered_datasets, included_dataset_names = [], [] for dataset, weight in data_mix: @@ -169,42 +173,34 @@ def make_oxe_dataset_kwargs_and_weights( filtered_datasets.append((dataset, weight)) included_dataset_names.append(dataset) else: - print(f"Skipping duplicate: {(dataset, weight)}.") + logging.warning(f"Skipping duplicate: {(dataset, weight)}.") data_mix = filtered_datasets data_kwargs_list, weights = [], [] for dataset, weight in data_mix: - dataset_kwargs = copy.deepcopy(oxe_dataset_configs.OXE_DATASET_KWARGS[dataset]) - if ( - dataset_kwargs["action_encoding"] - is not oxe_dataset_configs.ActionEncoding.EEF_POS - ): - print( + dataset_kwargs = copy.deepcopy(OXE_DATASET_CONFIGS[dataset]) + if dataset_kwargs["action_encoding"] is not ActionEncoding.EEF_POS: + logging.warning( f"Skipping {dataset} since only EEF pose delta action encoding " f"is supported." ) continue # adjust loaded features in kwargs - dataset_kwargs["image_obs_keys"] = dataset_kwargs["image_obs_keys"][ - :n_third_person_cameras - ] + ( - dataset_kwargs["image_obs_keys"][-n_wrist_cameras:] - if n_wrist_cameras - else [] - ) + dataset_kwargs["image_obs_keys"] = [ + dataset_kwargs["image_obs_keys"][k] for k in load_camera_views + ] if not any([e is not None for e in dataset_kwargs["image_obs_keys"]]): - print(f"Skipping {dataset} since no image input was loaded from it.") + logging.warning( + f"Skipping {dataset} since no image input was loaded from it." + ) continue - dataset_kwargs["depth_obs_keys"] = dataset_kwargs["depth_obs_keys"][ - :n_third_person_cameras - ] + ( - dataset_kwargs["depth_obs_keys"][-n_wrist_cameras:] - if n_wrist_cameras - else [] - ) + dataset_kwargs["depth_obs_keys"] = [ + dataset_kwargs["depth_obs_keys"][k] for k in load_camera_views + ] + if not load_depth: dataset_kwargs.pop("depth_obs_keys") if not load_proprio: diff --git a/train.py b/train.py index b0f779d8..42426b6d 100644 --- a/train.py +++ b/train.py @@ -23,7 +23,7 @@ import orca from orca.data.dataset import make_interleaved_dataset -from orca.data.oxe.oxe_dataset_mixes import make_oxe_dataset_kwargs_and_weights, mixes +from orca.data.oxe.oxe_dataset_mixes import make_oxe_dataset_kwargs_and_weights from orca.data.utils.text_processing import text_processors from orca.model import create_model_def from orca.model.components.hf_weight_loaders import weights_loaders @@ -151,15 +151,13 @@ def process_batch(batch): # load datasets if "oxe_kwargs" in FLAGS.config.dataset_kwargs: # create dataset_kwargs_list from oxe_kwargs - oxe_kwargs = FLAGS.config.dataset_kwargs["oxe_kwargs"].to_dict() - del FLAGS.config.dataset_kwargs["oxe_kwargs"] - oxe_kwargs["data_mix"] = mixes[oxe_kwargs["data_mix"]] ( - dataset_kwargs_list, - dataset_sampling_weights, - ) = make_oxe_dataset_kwargs_and_weights(**oxe_kwargs) - FLAGS.config.dataset_kwargs["dataset_kwargs_list"] = dataset_kwargs_list - FLAGS.config.dataset_kwargs["sample_weights"] = dataset_sampling_weights + FLAGS.config.dataset_kwargs["dataset_kwargs_list"], + FLAGS.config.dataset_kwargs["sample_weights"], + ) = make_oxe_dataset_kwargs_and_weights( + **FLAGS.config.dataset_kwargs["oxe_kwargs"] + ) + del FLAGS.config.dataset_kwargs["oxe_kwargs"] # override each element of dataset_kwargs_list with common_dataset_kwargs if "common_dataset_kwargs" in FLAGS.config.dataset_kwargs: From dc55881b3cf00b88711f5ceb72364aa655c3bb08 Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Thu, 7 Dec 2023 13:46:41 -0800 Subject: [PATCH 05/25] Add "tasks" -> "task" shim --- orca/utils/pretrained_utils.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/orca/utils/pretrained_utils.py b/orca/utils/pretrained_utils.py index 67130327..9498090d 100644 --- a/orca/utils/pretrained_utils.py +++ b/orca/utils/pretrained_utils.py @@ -182,6 +182,9 @@ def load_pretrained( ) with tf.io.gfile.GFile(orig_example_batch_path, "rb") as f: orig_example_batch = flax.serialization.msgpack_restore(f.read()) + # TODO: temporary shim for migrating from "tasks" to "task" + if "tasks" in orig_example_batch: + orig_example_batch["task"] = orig_example_batch.pop("tasks") if example_batch is not None: logging.info( "Checking differences between provided example_batch and pre-trained model example_batch..." From f97d0ce4f33fa8138021e2bf03965e9441aa0efa Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Thu, 7 Dec 2023 14:07:46 -0800 Subject: [PATCH 06/25] Consolidate OXE stuff, push standardize_fn loading out of orca.data --- experiments/dibya/finetune_config.py | 7 +- .../custom_standardization_transforms.py | 32 +++++ experiments/kevin/golden_config.py | 3 - finetune.py | 12 +- orca/data/dataset.py | 15 +-- orca/data/oxe/oxe_dataset_mixes.py | 28 ++--- ...s.py => oxe_standardization_transforms.py} | 73 +++++++++++ orca/data/standardization_transforms.py | 116 ------------------ tests/debug_config.py | 1 - 9 files changed, 134 insertions(+), 153 deletions(-) create mode 100644 experiments/kevin/custom_standardization_transforms.py rename orca/data/oxe/{oxe_dataset_transforms.py => oxe_standardization_transforms.py} (85%) delete mode 100644 orca/data/standardization_transforms.py diff --git a/experiments/dibya/finetune_config.py b/experiments/dibya/finetune_config.py index c69d7b80..87c8e53c 100644 --- a/experiments/dibya/finetune_config.py +++ b/experiments/dibya/finetune_config.py @@ -2,8 +2,6 @@ from ml_collections import ConfigDict from ml_collections.config_dict import FieldReference, placeholder -from orca.data.utils.data_utils import ActionEncoding, StateEncoding - @wrap def get_config( @@ -25,9 +23,10 @@ def get_config( "data_dir": "./tests/debug_dataset", "image_obs_keys": ["image_0", None], "state_obs_keys": ["state", None], - "state_encoding": StateEncoding.POS_EULER, - "action_encoding": ActionEncoding.EEF_POS, "action_proprio_normalization_type": "normal", + # standardize_fn is dynamically loaded from a file + # for example: "experiments/kevin/custom_standardization_transforms.py:aloha_dataset_transform" + "standardize_fn": "orca/data/oxe/oxe_standardization_transforms.py:bridge_dataset_transform", # If the default data loading speed is too slow, try these: # "num_parallel_reads": 8, # for reading from disk / GCS # "num_parallel_calls": 16, # for initial dataset construction diff --git a/experiments/kevin/custom_standardization_transforms.py b/experiments/kevin/custom_standardization_transforms.py new file mode 100644 index 00000000..9078b913 --- /dev/null +++ b/experiments/kevin/custom_standardization_transforms.py @@ -0,0 +1,32 @@ +"""Episode transforms for custom (non-OXE) RLDS datasets to canonical dataset definition.""" +from typing import Any, Dict + +import tensorflow as tf + + +def r2_d2_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: + # every input feature is batched, ie has leading batch dimension + trajectory["action"] = tf.concat( + ( + trajectory["action_dict"]["cartesian_velocity"], + trajectory["action_dict"]["gripper_velocity"], + ), + axis=-1, + ) + return trajectory + + +def fmb_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: + # every input feature is batched, ie has leading batch dimension + trajectory["observation"]["state"] = tf.concat( + ( + trajectory["observation"]["state"], + trajectory["observation"]["gripper_state"][..., None], + ), + axis=-1, + ) + return trajectory + + +def aloha_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: + return trajectory diff --git a/experiments/kevin/golden_config.py b/experiments/kevin/golden_config.py index 91aa3a0c..b48e8bcf 100644 --- a/experiments/kevin/golden_config.py +++ b/experiments/kevin/golden_config.py @@ -1,5 +1,3 @@ -import copy - from config import get_config as get_base_config from config import update_config, wrap @@ -62,7 +60,6 @@ def get_config(config_string=None): oxe_kwargs=dict( data_mix="oxe_magic_soup", data_dir="gs://rail-orca-central2/resize_256_256", - n_wrist_cameras=1, ), batch_size=256, shuffle_buffer_size=500000, diff --git a/finetune.py b/finetune.py index d3872292..73becd8b 100644 --- a/finetune.py +++ b/finetune.py @@ -1,5 +1,6 @@ import datetime from functools import partial +import imp import json import os @@ -153,12 +154,21 @@ def process_batch(batch): del batch["dataset_name"] return batch + # load standardize_fn from `path/to/file.py:fn_name` format + if ( + standardize_fn := FLAGS.config["dataset_kwargs"].get("standardize_fn", None) + ) is not None: + path, name = standardize_fn.split(":") + # imp is deprecated, but it's also what ml_collections uses + standardize_fn = getattr(imp.load_source("standardize_fn", path), name) + del FLAGS.config["dataset_kwargs"]["standardize_fn"] + FLAGS.config["dataset_kwargs"]["standardize_fn"] = standardize_fn + dataset = make_single_dataset( FLAGS.config.dataset_kwargs, FLAGS.config.traj_transform_kwargs, FLAGS.config.frame_transform_kwargs, train=True, - frame_transform_threads=FLAGS.config.frame_transform_threads, ) train_data_iter = ( dataset.repeat() diff --git a/orca/data/dataset.py b/orca/data/dataset.py index 0d3630f5..4b3c3bc5 100644 --- a/orca/data/dataset.py +++ b/orca/data/dataset.py @@ -10,7 +10,6 @@ import tensorflow as tf import tensorflow_datasets as tfds -from orca.data.standardization_transforms import RLDS_STANDARDIZATION_TRANSFORMS from orca.data.utils import bc_goal_relabeling, task_augmentation from orca.data.utils.data_utils import ( allocate_threads, @@ -545,9 +544,6 @@ def make_single_dataset( """ dataset, dataset_statistics = make_dataset_from_rlds( **dataset_kwargs, - standardize_fn=RLDS_STANDARDIZATION_TRANSFORMS.get( - dataset_kwargs["name"], None - ), train=train, ) dataset = apply_trajectory_transforms(dataset, **traj_transform_kwargs, train=train) @@ -602,13 +598,7 @@ def make_interleaved_dataset( dataset_sizes = [] all_dataset_statistics = [] for dataset_kwargs in dataset_kwargs_list: - _, dataset_statistics = make_dataset_from_rlds( - **dataset_kwargs, - standardize_fn=RLDS_STANDARDIZATION_TRANSFORMS.get( - dataset_kwargs["name"], None - ), - train=train, - ) + _, dataset_statistics = make_dataset_from_rlds(**dataset_kwargs, train=train) dataset_sizes.append(dataset_statistics["num_transitions"]) all_dataset_statistics.append(dataset_statistics) @@ -641,9 +631,6 @@ def make_interleaved_dataset( ): dataset, _ = make_dataset_from_rlds( **dataset_kwargs, - standardize_fn=RLDS_STANDARDIZATION_TRANSFORMS.get( - dataset_kwargs["name"], None - ), train=train, num_parallel_calls=threads, num_parallel_reads=reads, diff --git a/orca/data/oxe/oxe_dataset_mixes.py b/orca/data/oxe/oxe_dataset_mixes.py index a25768f4..168bc5e1 100755 --- a/orca/data/oxe/oxe_dataset_mixes.py +++ b/orca/data/oxe/oxe_dataset_mixes.py @@ -4,6 +4,7 @@ from typing import Any, Dict, List, Sequence, Tuple, Union from orca.data.oxe.oxe_dataset_configs import ActionEncoding, OXE_DATASET_CONFIGS +from orca.data.oxe.oxe_standardization_transforms import OXE_STANDARDIZATION_TRANSFORMS BRIDGE_MIX = [ ("bridge_dataset", 1.0), @@ -168,20 +169,20 @@ def make_oxe_dataset_kwargs_and_weights( if deduplicate: filtered_datasets, included_dataset_names = [], [] - for dataset, weight in data_mix: - if dataset not in included_dataset_names: - filtered_datasets.append((dataset, weight)) - included_dataset_names.append(dataset) + for name, weight in data_mix: + if name not in included_dataset_names: + filtered_datasets.append((name, weight)) + included_dataset_names.append(name) else: - logging.warning(f"Skipping duplicate: {(dataset, weight)}.") + logging.warning(f"Skipping duplicate: {(name, weight)}.") data_mix = filtered_datasets data_kwargs_list, weights = [], [] - for dataset, weight in data_mix: - dataset_kwargs = copy.deepcopy(OXE_DATASET_CONFIGS[dataset]) + for name, weight in data_mix: + dataset_kwargs = copy.deepcopy(OXE_DATASET_CONFIGS[name]) if dataset_kwargs["action_encoding"] is not ActionEncoding.EEF_POS: logging.warning( - f"Skipping {dataset} since only EEF pose delta action encoding " + f"Skipping {name} since only EEF pose delta action encoding " f"is supported." ) continue @@ -192,9 +193,7 @@ def make_oxe_dataset_kwargs_and_weights( ] if not any([e is not None for e in dataset_kwargs["image_obs_keys"]]): - logging.warning( - f"Skipping {dataset} since no image input was loaded from it." - ) + logging.warning(f"Skipping {name} since no image input was loaded from it.") continue dataset_kwargs["depth_obs_keys"] = [ @@ -209,10 +208,11 @@ def make_oxe_dataset_kwargs_and_weights( del dataset_kwargs["state_encoding"] del dataset_kwargs["action_encoding"] + # get standardization transform + dataset_kwargs["standardize_fn"] = OXE_STANDARDIZATION_TRANSFORMS[name] + # add dataset to list - data_kwargs_list.append( - {"name": dataset, "data_dir": data_dir, **dataset_kwargs} - ) + data_kwargs_list.append({"name": name, "data_dir": data_dir, **dataset_kwargs}) weights.append(weight) return data_kwargs_list, weights diff --git a/orca/data/oxe/oxe_dataset_transforms.py b/orca/data/oxe/oxe_standardization_transforms.py similarity index 85% rename from orca/data/oxe/oxe_dataset_transforms.py rename to orca/data/oxe/oxe_standardization_transforms.py index 82aef780..52db8d67 100755 --- a/orca/data/oxe/oxe_dataset_transforms.py +++ b/orca/data/oxe/oxe_standardization_transforms.py @@ -16,6 +16,7 @@ import tensorflow as tf +import orca.data.bridge.bridge_utils as bridge from orca.data.utils.data_utils import ( binarize_gripper_actions, invert_gripper_actions, @@ -23,6 +24,22 @@ ) +def bridge_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: + trajectory["action"] = tf.concat( + [ + trajectory["action"][:, :6], + binarize_gripper_actions(trajectory["action"][:, -1])[:, None], + ], + axis=1, + ) + trajectory = bridge.relabel_actions(trajectory) + trajectory["observation"]["EEF_state"] = trajectory["observation"]["state"][:, :6] + trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][ + :, -1: + ] + return trajectory + + def rt1_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: # make gripper action absolute action, +1 = open, 0 = close gripper_action = trajectory["action"]["gripper_closedness_action"] @@ -783,3 +800,59 @@ def gnm_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: axis=-1, ) return trajectory + + +OXE_STANDARDIZATION_TRANSFORMS = { + "bridge_dataset": bridge_dataset_transform, + "fractal20220817_data": rt1_dataset_transform, + "kuka": kuka_dataset_transform, + "taco_play": taco_play_dataset_transform, + "jaco_play": jaco_play_dataset_transform, + "berkeley_cable_routing": berkeley_cable_routing_dataset_transform, + "roboturk": roboturk_dataset_transform, + "nyu_door_opening_surprising_effectiveness": nyu_door_opening_dataset_transform, + "viola": viola_dataset_transform, + "berkeley_autolab_ur5": berkeley_autolab_ur5_dataset_transform, + "toto": toto_dataset_transform, + "language_table": language_table_dataset_transform, + "columbia_cairlab_pusht_real": pusht_dataset_transform, + "stanford_kuka_multimodal_dataset_converted_externally_to_rlds": stanford_kuka_multimodal_dataset_transform, + "nyu_rot_dataset_converted_externally_to_rlds": nyu_rot_dataset_transform, + "stanford_hydra_dataset_converted_externally_to_rlds": stanford_hydra_dataset_transform, + "austin_buds_dataset_converted_externally_to_rlds": austin_buds_dataset_transform, + "nyu_franka_play_dataset_converted_externally_to_rlds": nyu_franka_play_dataset_transform, + "maniskill_dataset_converted_externally_to_rlds": maniskill_dataset_transform, + "furniture_bench_dataset_converted_externally_to_rlds": furniture_bench_dataset_transform, + "cmu_franka_exploration_dataset_converted_externally_to_rlds": cmu_franka_exploration_dataset_transform, + "ucsd_kitchen_dataset_converted_externally_to_rlds": ucsd_kitchen_dataset_transform, + "ucsd_pick_and_place_dataset_converted_externally_to_rlds": ucsd_pick_place_dataset_transform, + "austin_sailor_dataset_converted_externally_to_rlds": austin_sailor_dataset_transform, + "austin_sirius_dataset_converted_externally_to_rlds": austin_sirius_dataset_transform, + "bc_z": bc_z_dataset_transform, + "utokyo_pr2_opening_fridge_converted_externally_to_rlds": tokyo_pr2_opening_fridge_dataset_transform, + "utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds": tokyo_pr2_tabletop_manipulation_dataset_transform, + "utokyo_xarm_pick_and_place_converted_externally_to_rlds": utokyo_xarm_pick_place_dataset_transform, + "utokyo_xarm_bimanual_converted_externally_to_rlds": utokyo_xarm_bimanual_dataset_transform, + "robo_net": robo_net_dataset_transform, + "berkeley_mvp_converted_externally_to_rlds": berkeley_mvp_dataset_transform, + "berkeley_rpt_converted_externally_to_rlds": berkeley_rpt_dataset_transform, + "kaist_nonprehensile_converted_externally_to_rlds": kaist_nonprehensible_dataset_transform, + "stanford_mask_vit_converted_externally_to_rlds": stanford_mask_vit_dataset_transform, + "tokyo_u_lsmo_converted_externally_to_rlds": tokyo_lsmo_dataset_transform, + "dlr_sara_pour_converted_externally_to_rlds": dlr_sara_pour_dataset_transform, + "dlr_sara_grid_clamp_converted_externally_to_rlds": dlr_sara_grid_clamp_dataset_transform, + "dlr_edan_shared_control_converted_externally_to_rlds": dlr_edan_shared_control_dataset_transform, + "asu_table_top_converted_externally_to_rlds": asu_table_top_dataset_transform, + "stanford_robocook_converted_externally_to_rlds": robocook_dataset_transform, + "imperialcollege_sawyer_wrist_cam": imperial_wristcam_dataset_transform, + "iamlab_cmu_pickup_insert_converted_externally_to_rlds": iamlab_pick_insert_dataset_transform, + "uiuc_d3field": uiuc_d3field_dataset_transform, + "utaustin_mutex": utaustin_mutex_dataset_transform, + "berkeley_fanuc_manipulation": berkeley_fanuc_dataset_transform, + "cmu_playing_with_food": cmu_playing_with_food_dataset_transform, + "cmu_play_fusion": playfusion_dataset_transform, + "cmu_stretch": cmu_stretch_dataset_transform, + "berkeley_gnm_recon": gnm_dataset_transform, + "berkeley_gnm_cory_hall": gnm_dataset_transform, + "berkeley_gnm_sac_son": gnm_dataset_transform, +} diff --git a/orca/data/standardization_transforms.py b/orca/data/standardization_transforms.py deleted file mode 100644 index d6c280a4..00000000 --- a/orca/data/standardization_transforms.py +++ /dev/null @@ -1,116 +0,0 @@ -"""Episode transforms for different RLDS datasets to canonical dataset definition.""" -from typing import Any, Dict - -import tensorflow as tf - -import orca.data.bridge.bridge_utils as bridge -from orca.data.oxe import oxe_dataset_transforms as ox -from orca.data.utils.data_utils import binarize_gripper_actions - - -def r2_d2_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: - # every input feature is batched, ie has leading batch dimension - trajectory["action"] = tf.concat( - ( - trajectory["action_dict"]["cartesian_velocity"], - trajectory["action_dict"]["gripper_velocity"], - ), - axis=-1, - ) - return trajectory - - -def fmb_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: - # every input feature is batched, ie has leading batch dimension - trajectory["observation"]["state"] = tf.concat( - ( - trajectory["observation"]["state"], - trajectory["observation"]["gripper_state"][..., None], - ), - axis=-1, - ) - return trajectory - - -def bridge_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: - trajectory["action"] = tf.concat( - [ - trajectory["action"][:, :6], - binarize_gripper_actions(trajectory["action"][:, -1])[:, None], - ], - axis=1, - ) - trajectory = bridge.relabel_actions(trajectory) - trajectory["observation"]["EEF_state"] = trajectory["observation"]["state"][:, :6] - trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][ - :, -1: - ] - return trajectory - - -def aloha_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: - return trajectory - - -RLDS_STANDARDIZATION_TRANSFORMS = { - "r2_d2": r2_d2_dataset_transform, - "r2_d2_pen_cmu_rgb": r2_d2_dataset_transform, - "r2_d2_play_cmu_rgb": r2_d2_dataset_transform, - "r2_d2_pen": r2_d2_dataset_transform, - "fmb_dataset": fmb_dataset_transform, - "bridge_dataset": bridge_dataset_transform, - "aloha_screwdriver_dataset": aloha_dataset_transform, - "aloha_sim_cube_scripted_dataset": aloha_dataset_transform, - # Open X-Embodiment Datasets - "fractal20220817_data": ox.rt1_dataset_transform, - "kuka": ox.kuka_dataset_transform, - "taco_play": ox.taco_play_dataset_transform, - "jaco_play": ox.jaco_play_dataset_transform, - "berkeley_cable_routing": ox.berkeley_cable_routing_dataset_transform, - "roboturk": ox.roboturk_dataset_transform, - "nyu_door_opening_surprising_effectiveness": ox.nyu_door_opening_dataset_transform, - "viola": ox.viola_dataset_transform, - "berkeley_autolab_ur5": ox.berkeley_autolab_ur5_dataset_transform, - "toto": ox.toto_dataset_transform, - "language_table": ox.language_table_dataset_transform, - "columbia_cairlab_pusht_real": ox.pusht_dataset_transform, - "stanford_kuka_multimodal_dataset_converted_externally_to_rlds": ox.stanford_kuka_multimodal_dataset_transform, - "nyu_rot_dataset_converted_externally_to_rlds": ox.nyu_rot_dataset_transform, - "stanford_hydra_dataset_converted_externally_to_rlds": ox.stanford_hydra_dataset_transform, - "austin_buds_dataset_converted_externally_to_rlds": ox.austin_buds_dataset_transform, - "nyu_franka_play_dataset_converted_externally_to_rlds": ox.nyu_franka_play_dataset_transform, - "maniskill_dataset_converted_externally_to_rlds": ox.maniskill_dataset_transform, - "furniture_bench_dataset_converted_externally_to_rlds": ox.furniture_bench_dataset_transform, - "cmu_franka_exploration_dataset_converted_externally_to_rlds": ox.cmu_franka_exploration_dataset_transform, - "ucsd_kitchen_dataset_converted_externally_to_rlds": ox.ucsd_kitchen_dataset_transform, - "ucsd_pick_and_place_dataset_converted_externally_to_rlds": ox.ucsd_pick_place_dataset_transform, - "austin_sailor_dataset_converted_externally_to_rlds": ox.austin_sailor_dataset_transform, - "austin_sirius_dataset_converted_externally_to_rlds": ox.austin_sirius_dataset_transform, - "bc_z": ox.bc_z_dataset_transform, - "utokyo_pr2_opening_fridge_converted_externally_to_rlds": ox.tokyo_pr2_opening_fridge_dataset_transform, - "utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds": ox.tokyo_pr2_tabletop_manipulation_dataset_transform, - "utokyo_xarm_pick_and_place_converted_externally_to_rlds": ox.utokyo_xarm_pick_place_dataset_transform, - "utokyo_xarm_bimanual_converted_externally_to_rlds": ox.utokyo_xarm_bimanual_dataset_transform, - "robo_net": ox.robo_net_dataset_transform, - "berkeley_mvp_converted_externally_to_rlds": ox.berkeley_mvp_dataset_transform, - "berkeley_rpt_converted_externally_to_rlds": ox.berkeley_rpt_dataset_transform, - "kaist_nonprehensile_converted_externally_to_rlds": ox.kaist_nonprehensible_dataset_transform, - "stanford_mask_vit_converted_externally_to_rlds": ox.stanford_mask_vit_dataset_transform, - "tokyo_u_lsmo_converted_externally_to_rlds": ox.tokyo_lsmo_dataset_transform, - "dlr_sara_pour_converted_externally_to_rlds": ox.dlr_sara_pour_dataset_transform, - "dlr_sara_grid_clamp_converted_externally_to_rlds": ox.dlr_sara_grid_clamp_dataset_transform, - "dlr_edan_shared_control_converted_externally_to_rlds": ox.dlr_edan_shared_control_dataset_transform, - "asu_table_top_converted_externally_to_rlds": ox.asu_table_top_dataset_transform, - "stanford_robocook_converted_externally_to_rlds": ox.robocook_dataset_transform, - "imperialcollege_sawyer_wrist_cam": ox.imperial_wristcam_dataset_transform, - "iamlab_cmu_pickup_insert_converted_externally_to_rlds": ox.iamlab_pick_insert_dataset_transform, - "uiuc_d3field": ox.uiuc_d3field_dataset_transform, - "utaustin_mutex": ox.utaustin_mutex_dataset_transform, - "berkeley_fanuc_manipulation": ox.berkeley_fanuc_dataset_transform, - "cmu_playing_with_food": ox.cmu_playing_with_food_dataset_transform, - "cmu_play_fusion": ox.playfusion_dataset_transform, - "cmu_stretch": ox.cmu_stretch_dataset_transform, - "berkeley_gnm_recon": ox.gnm_dataset_transform, - "berkeley_gnm_cory_hall": ox.gnm_dataset_transform, - "berkeley_gnm_sac_son": ox.gnm_dataset_transform, -} diff --git a/tests/debug_config.py b/tests/debug_config.py index 994878af..854ca83d 100644 --- a/tests/debug_config.py +++ b/tests/debug_config.py @@ -40,7 +40,6 @@ def get_config(): ], "frame_transform_kwargs": { "resize_size": (128, 128), - "image_augment_kwargs": [None], "num_parallel_calls": 4, }, "traj_transform_threads": 1, # shared between all datasets From ebf62a9983b189b45df79fe6e1ce6fea5c4a8be7 Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Thu, 7 Dec 2023 14:08:58 -0800 Subject: [PATCH 07/25] Update delete_task_conditioning docstring --- orca/data/utils/task_augmentation.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/orca/data/utils/task_augmentation.py b/orca/data/utils/task_augmentation.py index 3254b9d1..aef67a6e 100644 --- a/orca/data/utils/task_augmentation.py +++ b/orca/data/utils/task_augmentation.py @@ -15,9 +15,9 @@ def delete_task_conditioning( """ Randomly chooses one group, and deletes all the keys in the task dictionary matching this pattern. - :param traj: A dictionary containing trajectory data. should have a "task" key. - :param switch_key_groups_probs: A list of tuples, where each tuple contains a list of patterns and their probability. - :return: A dictionary with keys zeroed out according to the specified probabilities. + Args: + traj: A dictionary containing trajectory data. should have a "task" key. + delete_key_groups_probs: A list of tuples, where each tuple contains a list of patterns and their probability. """ if tf.math.reduce_all(traj["task"]["language_instruction"] == ""): return traj From b56c6bae7807737e380ea3c0866db3efa20298c8 Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Thu, 7 Dec 2023 14:11:50 -0800 Subject: [PATCH 08/25] Remove jax dependency from text_processing.py --- orca/data/utils/text_processing.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/orca/data/utils/text_processing.py b/orca/data/utils/text_processing.py index fff45eff..366b60ce 100644 --- a/orca/data/utils/text_processing.py +++ b/orca/data/utils/text_processing.py @@ -1,7 +1,5 @@ from typing import Optional, Sequence -from flax.core import FrozenDict -import jax.numpy as jnp import numpy as np import tensorflow as tf @@ -81,10 +79,10 @@ def encode(self, strings: Sequence[str]): text=strings, **self.kwargs, ) - inputs["position_ids"] = jnp.expand_dims( - jnp.arange(inputs["input_ids"].shape[1]), axis=0 + inputs["position_ids"] = np.expand_dims( + np.arange(inputs["input_ids"].shape[1]), axis=0 ).repeat(inputs["input_ids"].shape[0], axis=0) - return FrozenDict(inputs) + return inputs text_processors = { From 64f4eff23e3b86752fdde95d7cd7c12108180668 Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Thu, 7 Dec 2023 14:34:12 -0800 Subject: [PATCH 09/25] Refactor and move stuff around --- orca/data/dataset.py | 203 +++--------------- orca/data/obs_transforms.py | 86 ++++++++ orca/data/traj_transforms.py | 82 +++++++ orca/data/utils/data_utils.py | 25 +-- ..._goal_relabeling.py => goal_relabeling.py} | 12 +- orca/data/utils/task_augmentation.py | 6 +- 6 files changed, 215 insertions(+), 199 deletions(-) create mode 100644 orca/data/obs_transforms.py create mode 100644 orca/data/traj_transforms.py rename orca/data/utils/{bc_goal_relabeling.py => goal_relabeling.py} (86%) diff --git a/orca/data/dataset.py b/orca/data/dataset.py index 4b3c3bc5..03446429 100644 --- a/orca/data/dataset.py +++ b/orca/data/dataset.py @@ -1,4 +1,3 @@ -import copy from functools import partial import inspect import json @@ -10,7 +9,8 @@ import tensorflow as tf import tensorflow_datasets as tfds -from orca.data.utils import bc_goal_relabeling, task_augmentation +from orca.data import obs_transforms, traj_transforms +from orca.data.utils import goal_relabeling, task_augmentation from orca.data.utils.data_utils import ( allocate_threads, get_dataset_statistics, @@ -21,159 +21,6 @@ ) -def _chunk_act_obs( - traj, - window_size, - additional_action_window_size=0, -): - """ - Chunks actions and observations into the given window_size. - - The "action" and "observation" keys are each given a new axis (at index 1) of size `window_size`. - """ - traj_len = tf.shape(traj["action"])[0] - chunk_indices = tf.broadcast_to( - tf.range(-window_size + 1, 1), [traj_len, window_size] - ) + tf.broadcast_to(tf.range(traj_len)[:, None], [traj_len, window_size]) - - action_chunk_indices = tf.broadcast_to( - tf.range(-window_size + 1, 1 + additional_action_window_size), - [traj_len, window_size + additional_action_window_size], - ) + tf.broadcast_to( - tf.range(traj_len)[:, None], - [traj_len, window_size + additional_action_window_size], - ) - - floored_chunk_indices = tf.maximum(chunk_indices, 0) - - if "task" in traj: - goal_timestep = traj["task"]["goal_timestep"] - else: - goal_timestep = tf.fill([traj_len], traj_len, dtype=tf.int32) - - floored_action_chunk_indices = tf.minimum( - tf.maximum(action_chunk_indices, 0), goal_timestep[:, None] - 1 - ) - - traj["observation"] = tf.nest.map_structure( - lambda x: tf.gather(x, floored_chunk_indices), traj["observation"] - ) - traj["action"] = tf.gather(traj["action"], floored_action_chunk_indices) - - # indicates whether an entire observation is padding - traj["observation"]["pad_mask"] = chunk_indices >= 0 - - # Actions past the goal timestep become no-ops - action_past_goal = action_chunk_indices > goal_timestep[:, None] - 1 - # zero_actions = make_neutral_actions(traj["action"], action_encoding) - # traj["action"] = tf.where( - # action_past_goal[:, :, None], zero_actions, traj["action"] - # ) - return traj - - -def _subsample(traj, subsample_length): - """Subsamples trajectories to the given length.""" - traj_len = tf.shape(traj["action"])[0] - if traj_len > subsample_length: - indices = tf.random.shuffle(tf.range(traj_len))[:subsample_length] - traj = tf.nest.map_structure(lambda x: tf.gather(x, indices), traj) - return traj - - -def _add_pad_mask_dict(traj): - """Adds a dictionary indicating which elements of the observation are padding. - - traj["observation"]["pad_mask_dict"] = {k: traj["observation"][k] is not padding} - """ - traj_len = tf.shape(traj["action"])[0] - pad_masks = {} - for key in traj["observation"]: - if traj["observation"][key].dtype == tf.string: - pad_masks[key] = tf.strings.length(traj["observation"][key]) != 0 - else: - pad_masks[key] = tf.ones([traj_len], dtype=tf.bool) - traj["observation"]["pad_mask_dict"] = pad_masks - return traj - - -def _decode_images(obs: dict) -> dict: - """Decodes images and depth images.""" - for key in obs: - if "image" in key: - if obs[key].dtype == tf.string: - if tf.strings.length(obs[key]) == 0: - # this is a padding image - obs[key] = tf.zeros((1, 1, 3), dtype=tf.uint8) - else: - obs[key] = tf.io.decode_image( - obs[key], expand_animations=False, dtype=tf.uint8 - ) - elif obs[key].dtype == tf.uint8: - pass - else: - raise ValueError( - f"Unsupported image dtype: found {key} with dtype {obs[key].dtype}" - ) - elif "depth" in key: - if obs[key].dtype == tf.string: - if tf.strings.length(obs[key]) == 0: - # this is a padding image - obs[key] = tf.zeros((1, 1), dtype=tf.float32) - else: - obs[key] = tf.io.decode_image( - obs[key], expand_animations=False, dtype=tf.float32 - )[..., 0] - elif obs[key].dtype == tf.float32: - pass - else: - raise ValueError( - f"Unsupported depth dtype: found {key} with dtype {obs[key].dtype}" - ) - return obs - - -def _augment(obs: dict, seed, augment_kwargs) -> dict: - """Augments images, skipping padding images.""" - num_image_keys = sum(["image" in key for key in obs]) - - if not isinstance(augment_kwargs, Sequence): - augment_kwargs = [copy.deepcopy(augment_kwargs)] * num_image_keys - - for i in range(num_image_keys): - if augment_kwargs[i] is not None: - key = f"image_{i}" - if obs["pad_mask_dict"][key]: - obs[key] = dl.transforms.augment_image( - obs[key], **augment_kwargs[i], seed=seed + i - ) - return obs - - -def _resize(obs: dict, resize_size, depth_resize_size) -> dict: - """Resizes images and depth images.""" - num_image_keys = sum(["image" in key for key in obs]) - num_depth_keys = sum(["depth" in key for key in obs]) - - if resize_size is None or isinstance(resize_size[0], int): - resize_size = [resize_size] * num_image_keys - if depth_resize_size is None or isinstance(depth_resize_size[0], int): - depth_resize_size = [depth_resize_size] * num_depth_keys - - for i in range(num_image_keys): - if resize_size[i] is not None: - key = f"image_{i}" - obs[key] = dl.transforms.resize_image(obs[key], size=resize_size[i]) - - for i in range(num_depth_keys): - if depth_resize_size[i] is not None: - key = f"depth_{i}" - obs[key] = dl.transforms.resize_depth_image( - obs[key], size=depth_resize_size[i] - ) - return obs - - def apply_trajectory_transforms( dataset: dl.DLataset, *, @@ -200,7 +47,7 @@ def apply_trajectory_transforms( dataset (dl.DLataset): The dataset to transform. train (bool): Whether the dataset is for training (affects subsampling). goal_relabeling_strategy (str, optional): The goal relabeling strategy to use, or None for - no goal relabeling. See `bc_goal_relabeling.py`. + no goal relabeling. See `goal_relabeling.py`. goal_relabeling_kwargs (dict, optional): Additional keyword arguments to pass to the goal relabeling function. window_size (int, optional): The length of the snippets that trajectories are chunked into. additional_action_window_size (int, optional): The number of additional actions beyond window_size to include @@ -232,39 +79,42 @@ def apply_trajectory_transforms( ) # marks which observations are padding - dataset = dataset.traj_map(_add_pad_mask_dict, num_parallel_calls) + dataset = dataset.traj_map(traj_transforms.add_pad_mask_dict, num_parallel_calls) # adds the "task" key if goal_relabeling_strategy is not None: dataset = dataset.traj_map( partial( - getattr(bc_goal_relabeling, goal_relabeling_strategy), + getattr(goal_relabeling, goal_relabeling_strategy), **goal_relabeling_kwargs, ), num_parallel_calls, ) - if "language_instruction" in dataset.element_spec: - - def move_language_instruction_to_task(traj): - traj["task"]["language_instruction"] = traj.pop("language_instruction") - traj["task"]["pad_mask_dict"]["language_instruction"] = ( - tf.strings.length(traj["task"]["language_instruction"]) != 0 - ) - return traj + if "language_instruction" in dataset.element_spec: - dataset = dataset.traj_map( - move_language_instruction_to_task, num_parallel_calls + def process_language_instruction(traj): + # move the "language_instruction" key into the "task" dict + if "task" not in traj: + traj["task"] = {} + traj["task"]["language_instruction"] = traj.pop("language_instruction") + # mark whether the language instruction is padding + traj["task"]["pad_mask_dict"]["language_instruction"] = ( + tf.strings.length(traj["task"]["language_instruction"]) != 0 ) + return traj + + dataset = dataset.traj_map(process_language_instruction, num_parallel_calls) if train and subsample_length is not None: dataset = dataset.traj_map( - partial(_subsample, subsample_length=subsample_length), num_parallel_calls + partial(traj_transforms.subsample, subsample_length=subsample_length), + num_parallel_calls, ) dataset = dataset.traj_map( partial( - _chunk_act_obs, + traj_transforms.chunk_act_obs, window_size=window_size, additional_action_window_size=additional_action_window_size, ), @@ -318,7 +168,8 @@ def apply_frame_transforms( # it to the chunked "observation" dict as well as the non-chunked "task" dict def apply_obs_transform(fn: Callable[[dict], dict], frame): # task is not chunked -- apply fn directly - frame["task"] = fn(frame["task"]) + if "task" in frame: + frame["task"] = fn(frame["task"]) # observation is chunked -- apply fn along first axis frame["observation"] = dl.vmap(fn)(frame["observation"]) return frame @@ -335,7 +186,7 @@ def apply_obs_transform(fn: Callable[[dict], dict], frame): # decode images (and depth images) dataset = dataset.frame_map( - partial(apply_obs_transform, _decode_images), num_parallel_calls + partial(apply_obs_transform, obs_transforms.decode_images), num_parallel_calls ) # resize images (and depth images) @@ -343,7 +194,9 @@ def apply_obs_transform(fn: Callable[[dict], dict], frame): partial( apply_obs_transform, partial( - _resize, resize_size=resize_size, depth_resize_size=depth_resize_size + obs_transforms.resize, + resize_size=resize_size, + depth_resize_size=depth_resize_size, ), ), num_parallel_calls, @@ -353,7 +206,9 @@ def apply_obs_transform(fn: Callable[[dict], dict], frame): # augment all images with the same seed, skipping padding images def aug(frame): seed = tf.random.uniform([2], maxval=tf.dtypes.int32.max, dtype=tf.int32) - aug_fn = partial(_augment, seed=seed, augment_kwargs=image_augment_kwargs) + aug_fn = partial( + obs_transforms.augment, seed=seed, augment_kwargs=image_augment_kwargs + ) return apply_obs_transform(aug_fn, frame) dataset = dataset.frame_map(aug, num_parallel_calls) diff --git a/orca/data/obs_transforms.py b/orca/data/obs_transforms.py new file mode 100644 index 00000000..7c6ef5ae --- /dev/null +++ b/orca/data/obs_transforms.py @@ -0,0 +1,86 @@ +""" +Contains observation-level transforms used in the orca data pipeline. These transforms operate on the +"observation" dictionary, and are applied at a per-frame level. +""" +import copy +from typing import Sequence + +import dlimp as dl +import tensorflow as tf + + +def decode_images(obs: dict) -> dict: + """Decodes images and depth images.""" + for key in obs: + if "image" in key: + if obs[key].dtype == tf.string: + if tf.strings.length(obs[key]) == 0: + # this is a padding image + obs[key] = tf.zeros((1, 1, 3), dtype=tf.uint8) + else: + obs[key] = tf.io.decode_image( + obs[key], expand_animations=False, dtype=tf.uint8 + ) + elif obs[key].dtype == tf.uint8: + pass + else: + raise ValueError( + f"Unsupported image dtype: found {key} with dtype {obs[key].dtype}" + ) + elif "depth" in key: + if obs[key].dtype == tf.string: + if tf.strings.length(obs[key]) == 0: + # this is a padding image + obs[key] = tf.zeros((1, 1), dtype=tf.float32) + else: + obs[key] = tf.io.decode_image( + obs[key], expand_animations=False, dtype=tf.float32 + )[..., 0] + elif obs[key].dtype == tf.float32: + pass + else: + raise ValueError( + f"Unsupported depth dtype: found {key} with dtype {obs[key].dtype}" + ) + return obs + + +def augment(obs: dict, seed, augment_kwargs) -> dict: + """Augments images, skipping padding images.""" + num_image_keys = sum(["image" in key for key in obs]) + + if not isinstance(augment_kwargs, Sequence): + augment_kwargs = [copy.deepcopy(augment_kwargs)] * num_image_keys + + for i in range(num_image_keys): + if augment_kwargs[i] is not None: + key = f"image_{i}" + if obs["pad_mask_dict"][key]: + obs[key] = dl.transforms.augment_image( + obs[key], **augment_kwargs[i], seed=seed + i + ) + return obs + + +def resize(obs: dict, resize_size, depth_resize_size) -> dict: + """Resizes images and depth images.""" + num_image_keys = sum(["image" in key for key in obs]) + num_depth_keys = sum(["depth" in key for key in obs]) + + if resize_size is None or isinstance(resize_size[0], int): + resize_size = [resize_size] * num_image_keys + if depth_resize_size is None or isinstance(depth_resize_size[0], int): + depth_resize_size = [depth_resize_size] * num_depth_keys + + for i in range(num_image_keys): + if resize_size[i] is not None: + key = f"image_{i}" + obs[key] = dl.transforms.resize_image(obs[key], size=resize_size[i]) + + for i in range(num_depth_keys): + if depth_resize_size[i] is not None: + key = f"depth_{i}" + obs[key] = dl.transforms.resize_depth_image( + obs[key], size=depth_resize_size[i] + ) + return obs diff --git a/orca/data/traj_transforms.py b/orca/data/traj_transforms.py new file mode 100644 index 00000000..a8e867a0 --- /dev/null +++ b/orca/data/traj_transforms.py @@ -0,0 +1,82 @@ +""" +Contains trajectory transforms used in the orca data pipeline. Trajectory transforms operate on a dictionary +that represents a single trajectory, meaning each tensor has the same leading dimension (the trajectory +length). +""" +import tensorflow as tf + + +def chunk_act_obs( + traj: dict, + window_size: int, + additional_action_window_size: int = 0, +) -> dict: + """ + Chunks actions and observations into the given window_size. + + The "action" and "observation" keys are each given a new axis (at index 1) of size `window_size`. + """ + traj_len = tf.shape(traj["action"])[0] + chunk_indices = tf.broadcast_to( + tf.range(-window_size + 1, 1), [traj_len, window_size] + ) + tf.broadcast_to(tf.range(traj_len)[:, None], [traj_len, window_size]) + + action_chunk_indices = tf.broadcast_to( + tf.range(-window_size + 1, 1 + additional_action_window_size), + [traj_len, window_size + additional_action_window_size], + ) + tf.broadcast_to( + tf.range(traj_len)[:, None], + [traj_len, window_size + additional_action_window_size], + ) + + floored_chunk_indices = tf.maximum(chunk_indices, 0) + + if "task" in traj: + goal_timestep = traj["task"]["goal_timestep"] + else: + goal_timestep = tf.fill([traj_len], traj_len, dtype=tf.int32) + + floored_action_chunk_indices = tf.minimum( + tf.maximum(action_chunk_indices, 0), goal_timestep[:, None] - 1 + ) + + traj["observation"] = tf.nest.map_structure( + lambda x: tf.gather(x, floored_chunk_indices), traj["observation"] + ) + traj["action"] = tf.gather(traj["action"], floored_action_chunk_indices) + + # indicates whether an entire observation is padding + traj["observation"]["pad_mask"] = chunk_indices >= 0 + + # Actions past the goal timestep become no-ops + action_past_goal = action_chunk_indices > goal_timestep[:, None] - 1 + # zero_actions = make_neutral_actions(traj["action"], action_encoding) + # traj["action"] = tf.where( + # action_past_goal[:, :, None], zero_actions, traj["action"] + # ) + return traj + + +def subsample(traj: dict, subsample_length: int) -> dict: + """Subsamples trajectories to the given length.""" + traj_len = tf.shape(traj["action"])[0] + if traj_len > subsample_length: + indices = tf.random.shuffle(tf.range(traj_len))[:subsample_length] + traj = tf.nest.map_structure(lambda x: tf.gather(x, indices), traj) + return traj + + +def add_pad_mask_dict(traj: dict) -> dict: + """Adds a dictionary indicating which elements of the observation are padding. + + traj["observation"]["pad_mask_dict"] = {k: traj["observation"][k] is not padding} + """ + traj_len = tf.shape(traj["action"])[0] + pad_masks = {} + for key in traj["observation"]: + if traj["observation"][key].dtype == tf.string: + pad_masks[key] = tf.strings.length(traj["observation"][key]) != 0 + else: + pad_masks[key] = tf.ones([traj_len], dtype=tf.bool) + traj["observation"]["pad_mask_dict"] = pad_masks + return traj diff --git a/orca/data/utils/data_utils.py b/orca/data/utils/data_utils.py index e5b12cec..5ebc1a11 100644 --- a/orca/data/utils/data_utils.py +++ b/orca/data/utils/data_utils.py @@ -39,7 +39,7 @@ def to_padding(tensor: tf.Tensor) -> tf.Tensor: def make_neutral_actions( action: tf.Tensor, absolute_action_mask: tf.Tensor ) -> tf.Tensor: - """Returns "neutral" actions, meaning relative actions are zeroed and absolute actions are retrained. + """Returns "neutral" actions, meaning relative actions are zeroed and absolute actions are retained. `absolute_action_mask` should be a 1D boolean mask that indicates which action dimensions are absolute. """ return tf.where( @@ -174,6 +174,7 @@ def get_dataset_statistics( def normalize_action_and_proprio( traj: dict, metadata: dict, normalization_type: NormalizationType ): + """Normalizes the action and proprio fields of a trajectory using the given metadata.""" # maps keys of `metadata` to corresponding keys in `traj` keys_to_normalize = { "action": "action", @@ -213,15 +214,12 @@ def normalize_action_and_proprio( def binarize_gripper_actions(actions: tf.Tensor) -> tf.Tensor: """Converts gripper actions from continous to binary values (0 and 1). - We exploit that fact that most of the time, the gripper is fully open (near - 1.0) or fully closed (near 0.0). As it transitions between the two, it - sometimes passes through a few intermediate values. We relabel those - intermediate values based on the state that is reached _after_ those - intermediate values. + We exploit that fact that most of the time, the gripper is fully open (near 1.0) or fully closed (near + 0.0). As it transitions between the two, it sometimes passes through a few intermediate values. We relabel + those intermediate values based on the state that is reached _after_ those intermediate values. - In the edge case that the trajectory ends with an intermediate value, we - give up on binarizing and relabel that chunk of intermediate values as - the last action in the trajectory. + In the edge case that the trajectory ends with an intermediate value, we give up on binarizing and relabel + that chunk of intermediate values as the last action in the trajectory. The scan implements the following code: @@ -254,8 +252,8 @@ def scan_fn(carry, i): def rel2abs_gripper_actions(actions: tf.Tensor): - """ - Converts relative actions (-1 for closing, +1 for opening) to absolute gripper actions in range [0...1]. + """Converts relative actions (-1 for closing, +1 for opening) to absolute gripper actions in range + [0...1]. """ abs_actions = tf.math.cumsum(actions, axis=0) abs_actions = tf.clip_by_value(abs_actions, 0, 1) @@ -267,9 +265,8 @@ def invert_gripper_actions(actions: tf.Tensor): def allocate_threads(n: int, weights: np.ndarray): - """ - Allocates an integer n across an array based on weights. The final array sums to n, but each - element is no less than 1. + """Allocates an integer n across an array based on weights. The final array sums to n, but each element is + no less than 1. """ assert np.all(weights >= 0), "Weights must be non-negative" assert ( diff --git a/orca/data/utils/bc_goal_relabeling.py b/orca/data/utils/goal_relabeling.py similarity index 86% rename from orca/data/utils/bc_goal_relabeling.py rename to orca/data/utils/goal_relabeling.py index d73b3a6d..12c4b85a 100644 --- a/orca/data/utils/bc_goal_relabeling.py +++ b/orca/data/utils/goal_relabeling.py @@ -5,10 +5,8 @@ import tensorflow as tf -def uniform(traj): - """ - Relabels with a true uniform distribution over future states. - """ +def uniform(traj: dict) -> dict: + """Relabels with a true uniform distribution over future states.""" traj_len = tf.shape(tf.nest.flatten(traj["observation"])[0])[0] # select a random future index for each transition i in the range [i + 1, traj_len) @@ -30,10 +28,8 @@ def uniform(traj): return traj -def no_image_conditioning(traj): - """ - Relabels with empty goal images. - """ +def no_image_conditioning(traj: dict) -> dict: + """Relabels with empty goal images.""" traj_len = tf.shape(tf.nest.flatten(traj["observation"])[0])[0] traj["task"] = tf.nest.map_structure( lambda x: tf.zeros_like(x), diff --git a/orca/data/utils/task_augmentation.py b/orca/data/utils/task_augmentation.py index aef67a6e..b2a5ecc6 100644 --- a/orca/data/utils/task_augmentation.py +++ b/orca/data/utils/task_augmentation.py @@ -7,6 +7,8 @@ import tensorflow as tf +from orca.data.utils.data_utils import to_padding + def delete_task_conditioning( traj: Dict[str, Any], @@ -48,9 +50,7 @@ def delete_task_conditioning( for key in matching_keys: new_task[key] = tf.where( i == delete_group_idx, - tf.zeros_like(task[key]) - if tf.debugging.is_numeric_tensor(task[key]) - else "", + to_padding(task[key]), task[key], ) new_task["pad_mask_dict"][key] = tf.where( From 498d3db08a3282196bdf8ad01127fc3cd2d1c41a Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Thu, 7 Dec 2023 14:35:12 -0800 Subject: [PATCH 10/25] Bump dlimp to traj_map version --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index d91b709a..90952ecf 100644 --- a/requirements.txt +++ b/requirements.txt @@ -22,6 +22,6 @@ tensorflow_hub >= 0.14.0 tensorflow_text >= 2.13.0 tensorflow_datasets == 4.9.2 tensorflow_graphics == 2021.12.3 -dlimp @ git+https://github.com/kvablack/dlimp@9e29390ccb1404f9a6079207c8bdac53c9a7d4ef +dlimp @ git+https://github.com/kvablack/dlimp@50524cf198ff6330de32893e97651d29c3b70816 plotly >= 5.16.1 matplotlib From 37c5e4bf51ef6a89803057224016ab30d461abbb Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Thu, 7 Dec 2023 15:12:13 -0800 Subject: [PATCH 11/25] Move `get_oxe...` function to top level of oxe module --- orca/data/oxe/__init__.py | 84 ++++++++++++++++++++++++++++++ orca/data/oxe/oxe_dataset_mixes.py | 82 ----------------------------- train.py | 2 +- 3 files changed, 85 insertions(+), 83 deletions(-) diff --git a/orca/data/oxe/__init__.py b/orca/data/oxe/__init__.py index e69de29b..d022b70f 100755 --- a/orca/data/oxe/__init__.py +++ b/orca/data/oxe/__init__.py @@ -0,0 +1,84 @@ +import copy +import logging +from typing import Any, Dict, List, Sequence, Tuple, Union + +from orca.data.oxe.oxe_dataset_configs import ActionEncoding, OXE_DATASET_CONFIGS +from orca.data.oxe.oxe_dataset_mixes import OXE_NAMED_MIXES +from orca.data.oxe.oxe_standardization_transforms import OXE_STANDARDIZATION_TRANSFORMS + + +def make_oxe_dataset_kwargs_and_weights( + data_mix: Union[str, Sequence[Tuple[str, float]]], + data_dir: str, + deduplicate: bool = True, + load_camera_views: Sequence[str] = ("primary",), + load_depth: bool = True, + load_proprio: bool = True, +) -> Tuple[Dict[str, Any], List[float]]: + """ + Generates dataset kwargs for a given dataset mix from the Open X-Embodiment dataset. + + Args: + data_mix: List of (dataset name, sampling weight) tuples, or a string specifying a pre-defined mix to + load from `OXE_NAMED_MIXES`. + data_dir: Base data directory that gets registered in each dataset. + deduplicate: If True, discards any duplicate dataset entries based on dataset name. + load_camera_views: Which views to load from each dataset. See the top of `oxe_dataset_configs.py` + for available views. + load_depth: If True, loads corresponding depth channels for each RGB channel. + load_proprio: If True, loads proprioceptive information. + Returns: + Tuple of (dataset_kwargs_list, sampling weights). + """ + if isinstance(data_mix, str): + data_mix = OXE_NAMED_MIXES[data_mix] + + if deduplicate: + filtered_datasets, included_dataset_names = [], [] + for name, weight in data_mix: + if name not in included_dataset_names: + filtered_datasets.append((name, weight)) + included_dataset_names.append(name) + else: + logging.warning(f"Skipping duplicate: {(name, weight)}.") + data_mix = filtered_datasets + + data_kwargs_list, weights = [], [] + for name, weight in data_mix: + dataset_kwargs = copy.deepcopy(OXE_DATASET_CONFIGS[name]) + if dataset_kwargs["action_encoding"] is not ActionEncoding.EEF_POS: + logging.warning( + f"Skipping {name} since only EEF pose delta action encoding " + f"is supported." + ) + continue + + # adjust loaded features in kwargs + dataset_kwargs["image_obs_keys"] = [ + dataset_kwargs["image_obs_keys"][k] for k in load_camera_views + ] + + if not any([e is not None for e in dataset_kwargs["image_obs_keys"]]): + logging.warning(f"Skipping {name} since no image input was loaded from it.") + continue + + dataset_kwargs["depth_obs_keys"] = [ + dataset_kwargs["depth_obs_keys"][k] for k in load_camera_views + ] + + if not load_depth: + dataset_kwargs.pop("depth_obs_keys") + if not load_proprio: + dataset_kwargs.pop("state_obs_keys") + + del dataset_kwargs["state_encoding"] + del dataset_kwargs["action_encoding"] + + # get standardization transform + dataset_kwargs["standardize_fn"] = OXE_STANDARDIZATION_TRANSFORMS[name] + + # add dataset to list + data_kwargs_list.append({"name": name, "data_dir": data_dir, **dataset_kwargs}) + weights.append(weight) + + return data_kwargs_list, weights diff --git a/orca/data/oxe/oxe_dataset_mixes.py b/orca/data/oxe/oxe_dataset_mixes.py index 168bc5e1..5d5e28a5 100755 --- a/orca/data/oxe/oxe_dataset_mixes.py +++ b/orca/data/oxe/oxe_dataset_mixes.py @@ -1,10 +1,5 @@ """Defines dataset mixtures and weights for the Open X-Embodiment Datasets.""" -import copy -import logging -from typing import Any, Dict, List, Sequence, Tuple, Union -from orca.data.oxe.oxe_dataset_configs import ActionEncoding, OXE_DATASET_CONFIGS -from orca.data.oxe.oxe_standardization_transforms import OXE_STANDARDIZATION_TRANSFORMS BRIDGE_MIX = [ ("bridge_dataset", 1.0), @@ -139,80 +134,3 @@ "rtx_franka": RT_X_MIX + OXE_FRANKA_MIX, "oxe_magic_soup": OXE_MAGIC_SOUP, } - - -def make_oxe_dataset_kwargs_and_weights( - data_mix: Union[str, Sequence[Tuple[str, float]]], - data_dir: str, - deduplicate: bool = True, - load_camera_views: Sequence[str] = ("primary",), - load_depth: bool = True, - load_proprio: bool = True, -) -> Tuple[Dict[str, Any], List[float]]: - """ - Generates dataset kwargs for a given dataset mix from the Open X-Embodiment dataset. - - Args: - data_mix: List of (dataset name, sampling weight) tuples, or a string specifying a pre-defined mix to - load from `OXE_NAMED_MIXES` above. - data_dir: Base data directory that gets registered in each dataset. - deduplicate: If True, discards any duplicate dataset entries based on dataset name. - load_camera_views: Which views to load from each dataset. See the top of `oxe_dataset_configs.py` - for available views. - load_depth: If True, loads corresponding depth channels for each RGB channel. - load_proprio: If True, loads proprioceptive information. - Returns: - Tuple of (dataset_kwargs_list, sampling weights). - """ - if isinstance(data_mix, str): - data_mix = OXE_NAMED_MIXES[data_mix] - - if deduplicate: - filtered_datasets, included_dataset_names = [], [] - for name, weight in data_mix: - if name not in included_dataset_names: - filtered_datasets.append((name, weight)) - included_dataset_names.append(name) - else: - logging.warning(f"Skipping duplicate: {(name, weight)}.") - data_mix = filtered_datasets - - data_kwargs_list, weights = [], [] - for name, weight in data_mix: - dataset_kwargs = copy.deepcopy(OXE_DATASET_CONFIGS[name]) - if dataset_kwargs["action_encoding"] is not ActionEncoding.EEF_POS: - logging.warning( - f"Skipping {name} since only EEF pose delta action encoding " - f"is supported." - ) - continue - - # adjust loaded features in kwargs - dataset_kwargs["image_obs_keys"] = [ - dataset_kwargs["image_obs_keys"][k] for k in load_camera_views - ] - - if not any([e is not None for e in dataset_kwargs["image_obs_keys"]]): - logging.warning(f"Skipping {name} since no image input was loaded from it.") - continue - - dataset_kwargs["depth_obs_keys"] = [ - dataset_kwargs["depth_obs_keys"][k] for k in load_camera_views - ] - - if not load_depth: - dataset_kwargs.pop("depth_obs_keys") - if not load_proprio: - dataset_kwargs.pop("state_obs_keys") - - del dataset_kwargs["state_encoding"] - del dataset_kwargs["action_encoding"] - - # get standardization transform - dataset_kwargs["standardize_fn"] = OXE_STANDARDIZATION_TRANSFORMS[name] - - # add dataset to list - data_kwargs_list.append({"name": name, "data_dir": data_dir, **dataset_kwargs}) - weights.append(weight) - - return data_kwargs_list, weights diff --git a/train.py b/train.py index 42426b6d..22b4d130 100644 --- a/train.py +++ b/train.py @@ -23,7 +23,7 @@ import orca from orca.data.dataset import make_interleaved_dataset -from orca.data.oxe.oxe_dataset_mixes import make_oxe_dataset_kwargs_and_weights +from orca.data.oxe import make_oxe_dataset_kwargs_and_weights from orca.data.utils.text_processing import text_processors from orca.model import create_model_def from orca.model.components.hf_weight_loaders import weights_loaders From ba3ff8121529e48bef078a794272bc41d6d378f5 Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Thu, 7 Dec 2023 16:46:18 -0800 Subject: [PATCH 12/25] Fix catastrophic subsample before chunk bug --- orca/data/dataset.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/orca/data/dataset.py b/orca/data/dataset.py index 03446429..163c8674 100644 --- a/orca/data/dataset.py +++ b/orca/data/dataset.py @@ -106,12 +106,6 @@ def process_language_instruction(traj): dataset = dataset.traj_map(process_language_instruction, num_parallel_calls) - if train and subsample_length is not None: - dataset = dataset.traj_map( - partial(traj_transforms.subsample, subsample_length=subsample_length), - num_parallel_calls, - ) - dataset = dataset.traj_map( partial( traj_transforms.chunk_act_obs, @@ -121,6 +115,12 @@ def process_language_instruction(traj): num_parallel_calls, ) + if train and subsample_length is not None: + dataset = dataset.traj_map( + partial(traj_transforms.subsample, subsample_length=subsample_length), + num_parallel_calls, + ) + return dataset From 37881e5deb18384aee60ea9cdb9f6631ac1d2270 Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Sat, 9 Dec 2023 14:08:51 -0800 Subject: [PATCH 13/25] Change image_obs_keys to be named instead of numbered --- orca/data/dataset.py | 109 +++++++++++++++++------------------- orca/data/obs_transforms.py | 64 ++++++++++++--------- orca/data/oxe/__init__.py | 18 +++--- 3 files changed, 99 insertions(+), 92 deletions(-) diff --git a/orca/data/dataset.py b/orca/data/dataset.py index 163c8674..54597cce 100644 --- a/orca/data/dataset.py +++ b/orca/data/dataset.py @@ -1,7 +1,7 @@ from functools import partial import inspect import json -from typing import Callable, List, Optional, Sequence, Tuple, Union +from typing import Callable, List, Mapping, Optional, Sequence, Tuple, Union from absl import logging import dlimp as dl @@ -128,13 +128,9 @@ def apply_frame_transforms( dataset: dl.DLataset, *, train: bool, - image_augment_kwargs: Union[Optional[dict], Sequence[Optional[dict]]] = None, - resize_size: Union[ - Optional[Tuple[int, int]], Sequence[Optional[Tuple[int, int]]] - ] = None, - depth_resize_size: Union[ - Optional[Tuple[int, int]], Sequence[Optional[Tuple[int, int]]] - ] = None, + image_augment_kwargs: Union[dict, Mapping[str, dict]] = {}, + resize_size: Union[Tuple[int, int], Mapping[str, Tuple[int, int]]] = {}, + depth_resize_size: Union[Tuple[int, int], Mapping[str, Tuple[int, int]]] = {}, task_augment_strategy: Optional[str] = None, task_augment_kwargs: dict = {}, num_parallel_calls: int = tf.data.AUTOTUNE, @@ -145,17 +141,16 @@ def apply_frame_transforms( Args: train (bool): Whether the dataset is for training (affects image augmentation). dataset (dl.DLataset): The dataset to transform. - image_augment_kwargs (dict|Sequence[dict]): Keyword arguments to pass to the image augmentation - function. See `dlimp.transforms.augment_image` for documentation of these kwargs. If a list of dicts - is provided, then the ith entry will be used for "image_i" (order determined by `image_obs_keys` in - `make_dataset_from_rlds`). A None list entry will skip image augmentation for the corresponding - image(s). - resize_size (Tuple[int, int]|Sequence[Tuple[int, int]]): If provided, images will be - resized to this size. If a list of tuples is provided, then the ith entry will be used for - "image_i" and "depth_i" (order determined by `image_obs_keys` and `depth_obs_keys`, respectively, - in `make_dataset_from_rlds`). A value of None or a None list entry will skip resizing for the - corresponding image(s). - depth_resize_size (Tuple[int, int]|Sequence[Tuple[int, int]]): Same as resize_size, but for depth + image_augment_kwargs (dict|Mapping[str, dict]): Keyword arguments to pass to the image augmentation + function. See `dlimp.transforms.augment_image` for documentation of these kwargs. If a dict of + dicts is provided, then key "k" will be used for "image_{k}" (names determined by `image_obs_keys` + in `make_dataset_from_rlds`). Augmentation will be skipped for missing keys (so pass an empty dict + to skip augmentation for all images). + resize_size (Tuple[int, int]|Mapping[str, Tuple[int, int]]): If provided, images will be resized to + this size. If a dict of tuples is provided, then key "k" will be used for "image_{k}" (names + determined by `image_obs_keys` in `make_dataset_from_rlds`). Resizing will be skipped for missing + keys (so pass an empty dict to skip resizing for all images). + depth_resize_size (Tuple[int, int]|Mapping[str, Tuple[int, int]]): Same as resize_size, but for depth images. task_augmentation_strategy (str, optional): The task augmentation strategy to use, or None for no task augmentation. See `task_augmentation.py`. @@ -222,9 +217,9 @@ def make_dataset_from_rlds( train: bool, standardize_fn: Optional[Callable[[dict], dict]] = None, shuffle: bool = True, - image_obs_keys: Union[str, Sequence[str]] = (), - depth_obs_keys: Union[str, Sequence[str]] = (), - state_obs_keys: Union[str, Sequence[str]] = (), + image_obs_keys: Mapping[str, Optional[str]] = {}, + depth_obs_keys: Mapping[str, Optional[str]] = {}, + state_obs_keys: Sequence[Optional[str]] = (), action_proprio_normalization_type: NormalizationType = NormalizationType.NORMAL, dataset_statistics: Optional[Union[dict, str]] = None, num_parallel_reads: int = tf.data.AUTOTUNE, @@ -236,13 +231,18 @@ def make_dataset_from_rlds( If `standardize_fn` is provided, it will be applied to each trajectory. This function should get the trajectory into a standard format, which includes the keys "observation", "action", "is_terminal", and "is_last". "observation" should be a dictionary containing some number of additional keys, which will be - extracted into an even more standardized numbered format according to the "*_obs_keys" arguments. + extracted into an even more standardized format according to the "*_obs_keys" arguments. - For example, if the "observation" dict has the keys "image_workspace" and "image_wrist" after - `standardize_fn`, and `image_obs_keys=("image_workspace", None, "image_wrist")`, then the resulting - dataset will have an "observation" dict containing the keys "image_0", "image_1", and "image_2", where - "image_0" corresponds to "image_workspace", "image_1" is a padding image, and "image_2" corresponds to - "image_wrist". + The `image_obs_keys` and `depth_obs_keys` arguments are mappings from new names to old names, or None in + place of an old name to insert padding. For example, if after `standardize_fn`, your "observation" dict + has RGB images called "workspace" and "wrist", and `image_obs_keys={"primary": "workspace", "secondary": + None, "wrist": "wrist"}`, then the resulting dataset will have an "observation" dict containing the keys + "image_primary", "image_secondary", and "image_wrist", where "image_primary" corresponds to "workspace", + "image_secondary" is a padding image, and "image_wrist" corresponds to "wrist". + + `state_obs_keys` is a list of 1-dimensional proprioceptive keys to concatenate into a single array, which + will be placed in the "proprio" key of the "observation" dict. A single padding element (zero) will be + inserted for each None entry. Args: name (str): The name of the RLDS dataset (usually "name" or "name:version"). @@ -250,13 +250,15 @@ def make_dataset_from_rlds( train (bool): Whether to use the training or validation set. shuffle (bool, optional): Whether to shuffle the file read order (does NOT fully shuffle directories, since one file usually contains many trajectories!). - image_obs_keys (str|Sequence[str], optional): List of keys to be extracted from the "observation" - dict and mapped to "image_{i}". Inserts padding (an empty string) for each None entry. - depth_obs_keys (str|Sequence[str], optional): List of keys to be extracted from the "observation" - dict and mapped to "depth_{i}". Inserts padding (an empty string) for each None entry. - state_obs_keys (str|Sequence[str], optional): List of 1-dimensional proprioception keys to be - extracted from the "observation" dict, concatenated, and mapped to "proprio". Inserts 1 element of - padding (zero) for each None entry. + image_obs_keys (Mapping[str, str|None]): Mapping from {new: old} indicating which RGB images to + extract from the "observation" dict. `new_obs = {f"image_{new}": old_obs[old] for new, old in + image_obs_keys.items()}`. If a value of `old` is None, inserts a padding image instead (empty + string). + depth_obs_keys (Mapping[str, str|None]): Same as `image_obs_keys`, but for depth images. Keys will be + prefixed with "depth_" instead of "image_". + state_obs_keys (Sequence[str|None]): List of 1-dimensional proprioception keys to be extracted from + the "observation" dict, concatenated, and mapped to "proprio". Inserts 1 element of padding (zero) for + each None entry. action_proprio_normalization_type (str, optional): The type of normalization to perform on the action, proprio, or both. Can be "normal" (mean 0, std 1) or "bounds" (normalized to [-1, 1]). dataset_statistics: (dict|str, optional): dict (or path to JSON file) that contains dataset statistics @@ -269,13 +271,13 @@ def make_dataset_from_rlds( Returns: Dataset of trajectories where each step has the following fields: - observation: - - image_{0, 1, ..., N} # RGB image observations - - depth_{0, 1, ..., N} # depth image observations - - proprio # 1-dimensional array of proprioceptive observations - - action # action vector - - is_last # boolean indicator, 1 on last step - - is_terminal # boolean indicator, 1 on last step *if not timeout* - - language_instruction # string language instruction (optional) + - image_{name1, name2, ...} # RGB image observations + - depth_{name1, name2, ...} # depth image observations + - proprio # 1-dimensional array of proprioceptive observations + - action # action vector + - is_last # boolean indicator, 1 on last step + - is_terminal # boolean indicator, 1 on last step *if not timeout* + - language_instruction # string language instruction (optional) """ builder = tfds.builder(name, data_dir=data_dir) if "val" not in builder.info.splits: @@ -287,13 +289,6 @@ def make_dataset_from_rlds( builder, split=split, shuffle=shuffle, num_parallel_reads=num_parallel_reads ) - if not isinstance(image_obs_keys, Sequence): - image_obs_keys = [image_obs_keys] - if not isinstance(depth_obs_keys, Sequence): - depth_obs_keys = [depth_obs_keys] - if not isinstance(state_obs_keys, Sequence): - state_obs_keys = [state_obs_keys] - def restructure(traj): standard_keys = { "observation", @@ -320,17 +315,17 @@ def restructure(traj): traj_len = tf.shape(traj["action"])[0] old_obs = traj["observation"] new_obs = {} - for i, key in enumerate(image_obs_keys): - if key is None: - new_obs[f"image_{i}"] = tf.repeat("", traj_len) # padding + for new, old in image_obs_keys.items(): + if old is None: + new_obs[f"image_{new}"] = tf.repeat("", traj_len) # padding else: - new_obs[f"image_{i}"] = old_obs[key] + new_obs[f"image_{new}"] = old_obs[old] - for i, key in enumerate(depth_obs_keys): - if key is None: - new_obs[f"depth_{i}"] = tf.repeat("", traj_len) # padding + for new, old in depth_obs_keys.items(): + if old is None: + new_obs[f"depth_{new}"] = tf.repeat("", traj_len) # padding else: - new_obs[f"depth_{i}"] = old_obs[key] + new_obs[f"depth_{new}"] = old_obs[old] if state_obs_keys: new_obs["proprio"] = tf.concat( diff --git a/orca/data/obs_transforms.py b/orca/data/obs_transforms.py index 7c6ef5ae..710d1437 100644 --- a/orca/data/obs_transforms.py +++ b/orca/data/obs_transforms.py @@ -2,8 +2,8 @@ Contains observation-level transforms used in the orca data pipeline. These transforms operate on the "observation" dictionary, and are applied at a per-frame level. """ -import copy -from typing import Sequence +import logging +from typing import Mapping import dlimp as dl import tensorflow as tf @@ -47,40 +47,48 @@ def decode_images(obs: dict) -> dict: def augment(obs: dict, seed, augment_kwargs) -> dict: """Augments images, skipping padding images.""" - num_image_keys = sum(["image" in key for key in obs]) + image_names = {key[6:] for key in obs if key.startswith("image_")} - if not isinstance(augment_kwargs, Sequence): - augment_kwargs = [copy.deepcopy(augment_kwargs)] * num_image_keys + # "augment_order" is required in augment_kwargs, so if it's there, we can assume that the user has passed + # in a single augmentation dict (otherwise, we assume that the user has passed in a mapping from image + # name to augmentation dict) + if "augment_order" in augment_kwargs: + augment_kwargs = {name: augment_kwargs for name in image_names} + + for i, (name, kwargs) in enumerate(augment_kwargs.items()): + logging.debug(f"Augmenting image_{name} with kwargs {kwargs}") + obs[f"image_{name}"] = tf.cond( + obs["pad_mask_dict"][f"image_{name}"], + lambda: dl.transforms.augment_image( + obs[f"image_{name}"], + **kwargs, + seed=seed + i, # augment each image differently + ), + lambda: obs[f"image_{name}"], + ) - for i in range(num_image_keys): - if augment_kwargs[i] is not None: - key = f"image_{i}" - if obs["pad_mask_dict"][key]: - obs[key] = dl.transforms.augment_image( - obs[key], **augment_kwargs[i], seed=seed + i - ) return obs def resize(obs: dict, resize_size, depth_resize_size) -> dict: """Resizes images and depth images.""" - num_image_keys = sum(["image" in key for key in obs]) - num_depth_keys = sum(["depth" in key for key in obs]) + # just gets the part after "image_" or "depth_" + image_names = {key[6:] for key in obs if key.startswith("image_")} + depth_names = {key[6:] for key in obs if key.startswith("depth_")} + + if not isinstance(resize_size, Mapping): + resize_size = {name: resize_size for name in image_names} + if not isinstance(depth_resize_size, Mapping): + depth_resize_size = {name: depth_resize_size for name in depth_names} - if resize_size is None or isinstance(resize_size[0], int): - resize_size = [resize_size] * num_image_keys - if depth_resize_size is None or isinstance(depth_resize_size[0], int): - depth_resize_size = [depth_resize_size] * num_depth_keys + for name, size in resize_size.items(): + obs[f"image_{name}"] = dl.transforms.resize_image( + obs[f"image_{name}"], size=size + ) - for i in range(num_image_keys): - if resize_size[i] is not None: - key = f"image_{i}" - obs[key] = dl.transforms.resize_image(obs[key], size=resize_size[i]) + for name, size in depth_resize_size.items(): + obs[f"depth_{name}"] = dl.transforms.resize_depth_image( + obs[f"depth_{name}"], size=size + ) - for i in range(num_depth_keys): - if depth_resize_size[i] is not None: - key = f"depth_{i}" - obs[key] = dl.transforms.resize_depth_image( - obs[key], size=depth_resize_size[i] - ) return obs diff --git a/orca/data/oxe/__init__.py b/orca/data/oxe/__init__.py index d022b70f..29f622ac 100755 --- a/orca/data/oxe/__init__.py +++ b/orca/data/oxe/__init__.py @@ -54,17 +54,21 @@ def make_oxe_dataset_kwargs_and_weights( continue # adjust loaded features in kwargs - dataset_kwargs["image_obs_keys"] = [ - dataset_kwargs["image_obs_keys"][k] for k in load_camera_views - ] + dataset_kwargs["image_obs_keys"] = { + k: v + for k, v in dataset_kwargs["image_obs_keys"].items() + if k in load_camera_views + } - if not any([e is not None for e in dataset_kwargs["image_obs_keys"]]): + if not any([e is not None for e in dataset_kwargs["image_obs_keys"].values()]): logging.warning(f"Skipping {name} since no image input was loaded from it.") continue - dataset_kwargs["depth_obs_keys"] = [ - dataset_kwargs["depth_obs_keys"][k] for k in load_camera_views - ] + dataset_kwargs["depth_obs_keys"] = { + k: v + for k, v in dataset_kwargs["depth_obs_keys"].items() + if k in load_camera_views + } if not load_depth: dataset_kwargs.pop("depth_obs_keys") From 11bc0573d11aae6e2a66daee7a99c75eee0dde1e Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Sat, 9 Dec 2023 14:10:10 -0800 Subject: [PATCH 14/25] Remove is_last and is_terminal --- orca/data/dataset.py | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/orca/data/dataset.py b/orca/data/dataset.py index 54597cce..bc4cf7bf 100644 --- a/orca/data/dataset.py +++ b/orca/data/dataset.py @@ -229,9 +229,9 @@ def make_dataset_from_rlds( standardized format. Yields a dataset of trajectories. Does not include CPU-intensive operations. If `standardize_fn` is provided, it will be applied to each trajectory. This function should get the - trajectory into a standard format, which includes the keys "observation", "action", "is_terminal", and - "is_last". "observation" should be a dictionary containing some number of additional keys, which will be - extracted into an even more standardized format according to the "*_obs_keys" arguments. + trajectory into a standard format, which includes the keys "observation" and "action". "observation" + should be a dictionary containing some number of additional keys, which will be extracted into an even + more standardized format according to the "*_obs_keys" arguments. The `image_obs_keys` and `depth_obs_keys` arguments are mappings from new names to old names, or None in place of an old name to insert padding. For example, if after `standardize_fn`, your "observation" dict @@ -275,8 +275,6 @@ def make_dataset_from_rlds( - depth_{name1, name2, ...} # depth image observations - proprio # 1-dimensional array of proprioceptive observations - action # action vector - - is_last # boolean indicator, 1 on last step - - is_terminal # boolean indicator, 1 on last step *if not timeout* - language_instruction # string language instruction (optional) """ builder = tfds.builder(name, data_dir=data_dir) @@ -293,8 +291,6 @@ def restructure(traj): standard_keys = { "observation", "action", - "is_terminal", - "is_last", } # apply a standardization function, if provided From 7dc73f6237730f01a129e22dadc1b078ab1b8e99 Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Sat, 9 Dec 2023 14:14:29 -0800 Subject: [PATCH 15/25] Make timesteps 0-based indexing --- orca/data/dataset.py | 2 +- orca/data/traj_transforms.py | 6 +++--- orca/data/utils/goal_relabeling.py | 8 ++++---- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/orca/data/dataset.py b/orca/data/dataset.py index bc4cf7bf..8e2cbc55 100644 --- a/orca/data/dataset.py +++ b/orca/data/dataset.py @@ -335,7 +335,7 @@ def restructure(traj): ) # add timestep info - new_obs["timestep"] = tf.range(traj_len) + 1 + new_obs["timestep"] = tf.range(traj_len) traj["action"] = tf.cast(traj["action"], tf.float32) traj["observation"] = new_obs diff --git a/orca/data/traj_transforms.py b/orca/data/traj_transforms.py index a8e867a0..730439d2 100644 --- a/orca/data/traj_transforms.py +++ b/orca/data/traj_transforms.py @@ -34,10 +34,10 @@ def chunk_act_obs( if "task" in traj: goal_timestep = traj["task"]["goal_timestep"] else: - goal_timestep = tf.fill([traj_len], traj_len, dtype=tf.int32) + goal_timestep = tf.fill([traj_len - 1], traj_len, dtype=tf.int32) floored_action_chunk_indices = tf.minimum( - tf.maximum(action_chunk_indices, 0), goal_timestep[:, None] - 1 + tf.maximum(action_chunk_indices, 0), goal_timestep[:, None] ) traj["observation"] = tf.nest.map_structure( @@ -49,7 +49,7 @@ def chunk_act_obs( traj["observation"]["pad_mask"] = chunk_indices >= 0 # Actions past the goal timestep become no-ops - action_past_goal = action_chunk_indices > goal_timestep[:, None] - 1 + action_past_goal = action_chunk_indices > goal_timestep[:, None] # zero_actions = make_neutral_actions(traj["action"], action_encoding) # traj["action"] = tf.where( # action_past_goal[:, :, None], zero_actions, traj["action"] diff --git a/orca/data/utils/goal_relabeling.py b/orca/data/utils/goal_relabeling.py index 12c4b85a..04b7269f 100644 --- a/orca/data/utils/goal_relabeling.py +++ b/orca/data/utils/goal_relabeling.py @@ -22,8 +22,8 @@ def uniform(traj: dict) -> dict: lambda x: tf.gather(x, goal_idxs), traj["observation"], ) - traj["task"]["goal_timestep"] = goal_idxs + 1 - traj["task"]["end_timestep"] = tf.ones_like(goal_idxs) * traj_len + traj["task"]["goal_timestep"] = goal_idxs + traj["task"]["end_timestep"] = tf.fill([traj_len], traj_len - 1) return traj @@ -35,7 +35,7 @@ def no_image_conditioning(traj: dict) -> dict: lambda x: tf.zeros_like(x), traj["observation"], ) - traj["task"]["goal_timestep"] = tf.fill([traj_len], traj_len) - traj["task"]["end_timestep"] = tf.fill([traj_len], traj_len) + traj["task"]["goal_timestep"] = tf.fill([traj_len], traj_len - 1) + traj["task"]["end_timestep"] = tf.fill([traj_len], traj_len - 1) return traj From 7c9c9e13ad605528e6ef0fa59b20a75a1e34aebc Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Sat, 9 Dec 2023 20:43:01 -0800 Subject: [PATCH 16/25] Major refactors allowing for "language_instruction" and goal relabeling to be optional --- config.py | 8 +- experiments/kevin/golden_config.py | 24 +++--- orca/data/dataset.py | 118 +++++++++++++-------------- orca/data/obs_transforms.py | 106 ++++++++++++------------ orca/data/traj_transforms.py | 33 ++++---- orca/data/utils/data_utils.py | 12 +++ orca/data/utils/goal_relabeling.py | 26 ++---- orca/data/utils/task_augmentation.py | 87 ++++++++++---------- orca/utils/train_callbacks.py | 2 +- tests/debug_config.py | 2 +- 10 files changed, 208 insertions(+), 210 deletions(-) diff --git a/config.py b/config.py index 14e3c1e3..76d59765 100644 --- a/config.py +++ b/config.py @@ -111,10 +111,7 @@ def get_dataset_config(modality="multimodal", window_size=1): task_augmentation = dict( task_augment_strategy="delete_task_conditioning", task_augment_kwargs=dict( - delete_key_groups_probs=[ - (["image_*"], 0.5), - (["language_instruction"], 0.5), - ], + keep_image_prob=0.5, ), ) else: @@ -132,12 +129,14 @@ def get_dataset_config(modality="multimodal", window_size=1): # common_dataset_kwargs override specific kwargs from dataset_kwargs_list "common_dataset_kwargs": dict( action_proprio_normalization_type=normalization_type, + language_key="language_instruction", ), "traj_transform_kwargs": dict( window_size=window_size, additional_action_window_size=0, goal_relabeling_strategy="uniform", subsample_length=100, + **task_augmentation, ), "frame_transform_kwargs": dict( resize_size=(256, 256), @@ -156,7 +155,6 @@ def get_dataset_config(modality="multimodal", window_size=1): ], ), num_parallel_calls=200, - **task_augmentation, ), "traj_transform_threads": 48, # shared between all datasets "traj_read_threads": 48, # shared between all datasets diff --git a/experiments/kevin/golden_config.py b/experiments/kevin/golden_config.py index b48e8bcf..fd5fa09e 100644 --- a/experiments/kevin/golden_config.py +++ b/experiments/kevin/golden_config.py @@ -42,14 +42,14 @@ def get_config(config_string=None): del base_config["dataset_kwargs"]["frame_transform_kwargs"]["resize_size"] del base_config["dataset_kwargs"]["frame_transform_kwargs"]["image_augment_kwargs"] - base_config["dataset_kwargs"]["frame_transform_kwargs"]["resize_size"] = [ - (256, 256), # workspace (3rd person) camera is at 256x256 - (128, 128), # wrist camera is at 128x128 - ] - base_config["dataset_kwargs"]["frame_transform_kwargs"]["image_augment_kwargs"] = [ - workspace_augment_kwargs, - wrist_augment_kwargs, - ] + base_config["dataset_kwargs"]["frame_transform_kwargs"]["resize_size"] = { + "primary": (256, 256), # workspace camera is at 256x256 + "wrist": (128, 128), # wrist camera is at 128x128 + } + base_config["dataset_kwargs"]["frame_transform_kwargs"]["image_augment_kwargs"] = { + "primary": workspace_augment_kwargs, + "wrist": wrist_augment_kwargs, + } config = update_config( base_config, @@ -70,8 +70,8 @@ def get_config(config_string=None): "workspace": { "cls_name": "image_tokenizer", "kwargs": dict( - obs_stack_keys=["image_0"], - task_stack_keys=["image_0"], + obs_stack_keys=["image_primary"], + task_stack_keys=["image_primary"], task_film_keys=[], encoder="small-stem-16", ), @@ -79,8 +79,8 @@ def get_config(config_string=None): "wrist": { "cls_name": "image_tokenizer", "kwargs": dict( - obs_stack_keys=["image_1"], - task_stack_keys=["image_1"], + obs_stack_keys=["image_wrist"], + task_stack_keys=["image_wrist"], task_film_keys=[], encoder="small-stem-16", ), diff --git a/orca/data/dataset.py b/orca/data/dataset.py index 8e2cbc55..11a6ddd0 100644 --- a/orca/data/dataset.py +++ b/orca/data/dataset.py @@ -33,6 +33,8 @@ def apply_trajectory_transforms( skip_unlabeled: bool = False, max_action: Optional[float] = None, max_proprio: Optional[float] = None, + task_augment_strategy: Optional[str] = None, + task_augment_kwargs: dict = {}, num_parallel_calls: int = tf.data.AUTOTUNE, ) -> dl.DLataset: """Applies common transforms that happen at a trajectory level. Such transforms are usually some sort of @@ -59,11 +61,15 @@ def apply_trajectory_transforms( of *any* transition has an absolute value larger than this will be skipped. max_proprio: (float, optional): If provided, trajectories in which *any* proprio dimension of *any* transition has an absolute value larger than this will be skipped. + task_augment_strategy (str, optional): The task augmentation strategy to use, or None for no task + augmentation. See `task_augmentation.py`. + task_augment_kwargs (dict, optional): Additional keyword arguments to pass to the task augmentation + function. num_parallel_calls (int, optional): number of parallel calls for map operations. Default to AUTOTUNE. """ - if skip_unlabeled and "language_instruction" in dataset.element_spec: + if skip_unlabeled and "language_instruction" in dataset.element_spec["task"]: dataset = dataset.filter( - lambda x: tf.math.reduce_any(x["language_instruction"] != "") + lambda x: tf.math.reduce_any(x["task"]["language_instruction"] != "") ) if max_action is not None: @@ -78,10 +84,10 @@ def apply_trajectory_transforms( ) ) - # marks which observations are padding + # marks which entires of the observation and task dicts are padding dataset = dataset.traj_map(traj_transforms.add_pad_mask_dict, num_parallel_calls) - # adds the "task" key + # updates the "task" dict if goal_relabeling_strategy is not None: dataset = dataset.traj_map( partial( @@ -91,21 +97,18 @@ def apply_trajectory_transforms( num_parallel_calls, ) - if "language_instruction" in dataset.element_spec: - - def process_language_instruction(traj): - # move the "language_instruction" key into the "task" dict - if "task" not in traj: - traj["task"] = {} - traj["task"]["language_instruction"] = traj.pop("language_instruction") - # mark whether the language instruction is padding - traj["task"]["pad_mask_dict"]["language_instruction"] = ( - tf.strings.length(traj["task"]["language_instruction"]) != 0 - ) - return traj - - dataset = dataset.traj_map(process_language_instruction, num_parallel_calls) + # must run task augmentation before chunking, in case it changes goal timesteps + if train and task_augment_strategy is not None: + # perform task augmentation (e.g., dropping keys) + dataset = dataset.traj_map( + partial( + getattr(task_augmentation, task_augment_strategy), + **task_augment_kwargs, + ), + num_parallel_calls, + ) + # chunks actions and observations dataset = dataset.traj_map( partial( traj_transforms.chunk_act_obs, @@ -131,8 +134,6 @@ def apply_frame_transforms( image_augment_kwargs: Union[dict, Mapping[str, dict]] = {}, resize_size: Union[Tuple[int, int], Mapping[str, Tuple[int, int]]] = {}, depth_resize_size: Union[Tuple[int, int], Mapping[str, Tuple[int, int]]] = {}, - task_augment_strategy: Optional[str] = None, - task_augment_kwargs: dict = {}, num_parallel_calls: int = tf.data.AUTOTUNE, ) -> dl.DLataset: """Applies common transforms that happen at a frame level. These transforms are usually more @@ -152,10 +153,6 @@ def apply_frame_transforms( keys (so pass an empty dict to skip resizing for all images). depth_resize_size (Tuple[int, int]|Mapping[str, Tuple[int, int]]): Same as resize_size, but for depth images. - task_augmentation_strategy (str, optional): The task augmentation strategy to use, or None for no task - augmentation. See `task_augmentation.py`. - task_augmentation_kwargs (dict, optional): Additional keyword arguments to pass to the task - augmentation function. num_parallel_calls (int): number of parallel calls for frame_map operations. Default to AUTOTUNE. """ @@ -163,33 +160,17 @@ def apply_frame_transforms( # it to the chunked "observation" dict as well as the non-chunked "task" dict def apply_obs_transform(fn: Callable[[dict], dict], frame): # task is not chunked -- apply fn directly - if "task" in frame: - frame["task"] = fn(frame["task"]) + frame["task"] = fn(frame["task"]) # observation is chunked -- apply fn along first axis frame["observation"] = dl.vmap(fn)(frame["observation"]) return frame - if train and task_augment_strategy is not None: - # perform task augmentation (e.g., dropping keys) - dataset = dataset.frame_map( - partial( - getattr(task_augmentation, task_augment_strategy), - **task_augment_kwargs, - ), - num_parallel_calls, - ) - - # decode images (and depth images) - dataset = dataset.frame_map( - partial(apply_obs_transform, obs_transforms.decode_images), num_parallel_calls - ) - - # resize images (and depth images) + # decode + resize images (and depth images) dataset = dataset.frame_map( partial( apply_obs_transform, partial( - obs_transforms.resize, + obs_transforms.decode_and_resize, resize_size=resize_size, depth_resize_size=depth_resize_size, ), @@ -220,6 +201,7 @@ def make_dataset_from_rlds( image_obs_keys: Mapping[str, Optional[str]] = {}, depth_obs_keys: Mapping[str, Optional[str]] = {}, state_obs_keys: Sequence[Optional[str]] = (), + language_key: Optional[str] = None, action_proprio_normalization_type: NormalizationType = NormalizationType.NORMAL, dataset_statistics: Optional[Union[dict, str]] = None, num_parallel_reads: int = tf.data.AUTOTUNE, @@ -244,6 +226,9 @@ def make_dataset_from_rlds( will be placed in the "proprio" key of the "observation" dict. A single padding element (zero) will be inserted for each None entry. + The dataset will also include a "task" dict. If `language_key` is provided, then the "task" dict will + contain the key "language_instruction", extracted from `traj[language_key]`. + Args: name (str): The name of the RLDS dataset (usually "name" or "name:version"). data_dir (str): The path to the data directory. @@ -259,6 +244,8 @@ def make_dataset_from_rlds( state_obs_keys (Sequence[str|None]): List of 1-dimensional proprioception keys to be extracted from the "observation" dict, concatenated, and mapped to "proprio". Inserts 1 element of padding (zero) for each None entry. + language_key (str, optional): If provided, the "task" dict will contain the key + "language_instruction", extracted from `traj[language_key]`. action_proprio_normalization_type (str, optional): The type of normalization to perform on the action, proprio, or both. Can be "normal" (mean 0, std 1) or "bounds" (normalized to [-1, 1]). dataset_statistics: (dict|str, optional): dict (or path to JSON file) that contains dataset statistics @@ -274,8 +261,11 @@ def make_dataset_from_rlds( - image_{name1, name2, ...} # RGB image observations - depth_{name1, name2, ...} # depth image observations - proprio # 1-dimensional array of proprioceptive observations + - timestep # timestep of each frame + - task: + - language_instruction # language instruction, present if `language_key` is provided - action # action vector - - language_instruction # string language instruction (optional) + - dataset_name # name of the dataset """ builder = tfds.builder(name, data_dir=data_dir) if "val" not in builder.info.splits: @@ -287,26 +277,21 @@ def make_dataset_from_rlds( builder, split=split, shuffle=shuffle, num_parallel_reads=num_parallel_reads ) - def restructure(traj): - standard_keys = { - "observation", - "action", - } + REQUIRED_KEYS = {"observation", "action"} + if language_key is not None: + REQUIRED_KEYS.add(language_key) + def restructure(traj): # apply a standardization function, if provided if standardize_fn is not None: traj = standardize_fn(traj) - if not all(k in traj for k in standard_keys): + if not all(k in traj for k in REQUIRED_KEYS): raise ValueError( - f"Trajectory is missing keys: {standard_keys - set(traj.keys())}. " + f"Trajectory is missing keys: {REQUIRED_KEYS - set(traj.keys())}. " "Did you write a `standardize_fn`?" ) - # filter out keys that are not needed - allowed_keys = standard_keys | {"language_instruction"} - traj = {k: v for k, v in traj.items() if k in allowed_keys} - # extracts images, depth images and proprio from the "observation" dict traj_len = tf.shape(traj["action"])[0] old_obs = traj["observation"] @@ -337,13 +322,22 @@ def restructure(traj): # add timestep info new_obs["timestep"] = tf.range(traj_len) - traj["action"] = tf.cast(traj["action"], tf.float32) - traj["observation"] = new_obs - - # add name of dataset - traj["dataset_name"] = tf.repeat(name, traj_len) - - return traj + # extracts `language_key` into the "task" dict + task = {} + if language_key is not None: + if traj[language_key].dtype != tf.string: + raise ValueError( + f"Language key {language_key} has dtype {traj[language_key].dtype}, " + "but it must be tf.string." + ) + task["language_instruction"] = traj.pop(language_key) + + return { + "observation": new_obs, + "task": task, + "action": tf.cast(traj["action"], tf.float32), + "dataset_name": tf.repeat(name, traj_len), + } # load or compute dataset statistics if isinstance(dataset_statistics, str): diff --git a/orca/data/obs_transforms.py b/orca/data/obs_transforms.py index 710d1437..07364307 100644 --- a/orca/data/obs_transforms.py +++ b/orca/data/obs_transforms.py @@ -3,48 +3,11 @@ "observation" dictionary, and are applied at a per-frame level. """ import logging -from typing import Mapping import dlimp as dl import tensorflow as tf -def decode_images(obs: dict) -> dict: - """Decodes images and depth images.""" - for key in obs: - if "image" in key: - if obs[key].dtype == tf.string: - if tf.strings.length(obs[key]) == 0: - # this is a padding image - obs[key] = tf.zeros((1, 1, 3), dtype=tf.uint8) - else: - obs[key] = tf.io.decode_image( - obs[key], expand_animations=False, dtype=tf.uint8 - ) - elif obs[key].dtype == tf.uint8: - pass - else: - raise ValueError( - f"Unsupported image dtype: found {key} with dtype {obs[key].dtype}" - ) - elif "depth" in key: - if obs[key].dtype == tf.string: - if tf.strings.length(obs[key]) == 0: - # this is a padding image - obs[key] = tf.zeros((1, 1), dtype=tf.float32) - else: - obs[key] = tf.io.decode_image( - obs[key], expand_animations=False, dtype=tf.float32 - )[..., 0] - elif obs[key].dtype == tf.float32: - pass - else: - raise ValueError( - f"Unsupported depth dtype: found {key} with dtype {obs[key].dtype}" - ) - return obs - - def augment(obs: dict, seed, augment_kwargs) -> dict: """Augments images, skipping padding images.""" image_names = {key[6:] for key in obs if key.startswith("image_")} @@ -55,7 +18,10 @@ def augment(obs: dict, seed, augment_kwargs) -> dict: if "augment_order" in augment_kwargs: augment_kwargs = {name: augment_kwargs for name in image_names} - for i, (name, kwargs) in enumerate(augment_kwargs.items()): + for i, name in enumerate(image_names): + if name not in augment_kwargs: + continue + kwargs = augment_kwargs[name] logging.debug(f"Augmenting image_{name} with kwargs {kwargs}") obs[f"image_{name}"] = tf.cond( obs["pad_mask_dict"][f"image_{name}"], @@ -70,25 +36,65 @@ def augment(obs: dict, seed, augment_kwargs) -> dict: return obs -def resize(obs: dict, resize_size, depth_resize_size) -> dict: - """Resizes images and depth images.""" +def decode_and_resize(obs: dict, resize_size, depth_resize_size) -> dict: + """Decodes images and depth images, and then optionally resizes them.""" # just gets the part after "image_" or "depth_" image_names = {key[6:] for key in obs if key.startswith("image_")} depth_names = {key[6:] for key in obs if key.startswith("depth_")} - if not isinstance(resize_size, Mapping): + if isinstance(resize_size, tuple): resize_size = {name: resize_size for name in image_names} - if not isinstance(depth_resize_size, Mapping): + if isinstance(depth_resize_size, tuple): depth_resize_size = {name: depth_resize_size for name in depth_names} - for name, size in resize_size.items(): - obs[f"image_{name}"] = dl.transforms.resize_image( - obs[f"image_{name}"], size=size - ) + for name in image_names: + if name not in resize_size: + logging.warning( + f"No resize_size was provided for image_{name}. This will result in 1x1 " + "padding images, which may cause errors if you mix padding and non-padding images." + ) + image = obs[f"image_{name}"] + if image.dtype == tf.string: + if tf.strings.length(image) == 0: + # this is a padding image + image = tf.zeros((*resize_size.get(name, (1, 1)), 3), dtype=tf.uint8) + else: + image = tf.io.decode_image( + image, expand_animations=False, dtype=tf.uint8 + ) + elif image.dtype != tf.uint8: + raise ValueError( + f"Unsupported image dtype: found image_{name} with dtype {image.dtype}" + ) + if name in resize_size: + image = dl.transforms.resize_image(image, size=resize_size[name]) + obs[f"image_{name}"] = image - for name, size in depth_resize_size.items(): - obs[f"depth_{name}"] = dl.transforms.resize_depth_image( - obs[f"depth_{name}"], size=size - ) + for name in depth_names: + if name not in depth_resize_size: + logging.warning( + f"No depth_resize_size was provided for depth_{name}. This will result in 1x1 " + "padding depth images, which may cause errors if you mix padding and non-padding images." + ) + depth = obs[f"depth_{name}"] + if depth.dtype == tf.string: + if tf.strings.length(depth) == 0: + # this is a padding image + depth = tf.zeros( + (*depth_resize_size.get(name, (1, 1)), 1), dtype=tf.float32 + ) + else: + depth = tf.io.decode_image( + depth, expand_animations=False, dtype=tf.float32 + )[..., 0] + elif depth.dtype != tf.float32: + raise ValueError( + f"Unsupported depth dtype: found depth_{name} with dtype {depth.dtype}" + ) + if name in depth_resize_size: + depth = dl.transforms.resize_depth_image( + depth, size=depth_resize_size[name] + ) + obs[f"depth_{name}"] = depth return obs diff --git a/orca/data/traj_transforms.py b/orca/data/traj_transforms.py index 730439d2..4287b519 100644 --- a/orca/data/traj_transforms.py +++ b/orca/data/traj_transforms.py @@ -11,10 +11,10 @@ def chunk_act_obs( window_size: int, additional_action_window_size: int = 0, ) -> dict: - """ - Chunks actions and observations into the given window_size. + """Chunks actions and observations into the given window_size. - The "action" and "observation" keys are each given a new axis (at index 1) of size `window_size`. + "observation" keys are given a new axis (at index 1) of size `window_size`. "action" is given a new axis + (at index 1) of size `window_size + additional_action_window_size`. """ traj_len = tf.shape(traj["action"])[0] chunk_indices = tf.broadcast_to( @@ -31,10 +31,10 @@ def chunk_act_obs( floored_chunk_indices = tf.maximum(chunk_indices, 0) - if "task" in traj: - goal_timestep = traj["task"]["goal_timestep"] + if "timestep" in traj["task"]: + goal_timestep = traj["task"]["timestep"] else: - goal_timestep = tf.fill([traj_len - 1], traj_len, dtype=tf.int32) + goal_timestep = tf.fill([traj_len], traj_len - 1) floored_action_chunk_indices = tf.minimum( tf.maximum(action_chunk_indices, 0), goal_timestep[:, None] @@ -67,16 +67,19 @@ def subsample(traj: dict, subsample_length: int) -> dict: def add_pad_mask_dict(traj: dict) -> dict: - """Adds a dictionary indicating which elements of the observation are padding. + """Adds a dictionary indicating which elements of the observation/task should be treated as padding. - traj["observation"]["pad_mask_dict"] = {k: traj["observation"][k] is not padding} + traj["observation"|"task"]["pad_mask_dict"] = {k: traj["observation"|"task"][k] is not padding} """ traj_len = tf.shape(traj["action"])[0] - pad_masks = {} - for key in traj["observation"]: - if traj["observation"][key].dtype == tf.string: - pad_masks[key] = tf.strings.length(traj["observation"][key]) != 0 - else: - pad_masks[key] = tf.ones([traj_len], dtype=tf.bool) - traj["observation"]["pad_mask_dict"] = pad_masks + for key in ["observation", "task"]: + pad_mask_dict = {} + for subkey in traj[key]: + if traj[key][subkey].dtype == tf.string: + # handles "language_instruction", "image_*", and "depth_*" + pad_mask_dict[subkey] = tf.strings.length(traj[key][subkey]) != 0 + else: + # all other keys should not be treated as padding + pad_mask_dict[subkey] = tf.ones([traj_len], dtype=tf.bool) + traj[key]["pad_mask_dict"] = pad_mask_dict return traj diff --git a/orca/data/utils/data_utils.py b/orca/data/utils/data_utils.py index 5ebc1a11..6af1ed64 100644 --- a/orca/data/utils/data_utils.py +++ b/orca/data/utils/data_utils.py @@ -20,6 +20,18 @@ def tree_map(fn: Callable, tree: dict) -> dict: } +def tree_merge(*trees: dict) -> dict: + """Merges a list of nested dictionaries, with later dictionaries overriding earlier ones.""" + merged = {} + for tree in trees: + for k, v in tree.items(): + if isinstance(v, dict): + merged[k] = tree_merge(merged.get(k, {}), v) + else: + merged[k] = v + return merged + + class NormalizationType(str, Enum): """Defines supported normalization schemes for action and proprio.""" diff --git a/orca/data/utils/goal_relabeling.py b/orca/data/utils/goal_relabeling.py index 04b7269f..b4b663aa 100644 --- a/orca/data/utils/goal_relabeling.py +++ b/orca/data/utils/goal_relabeling.py @@ -1,9 +1,12 @@ """ Contains simple goal relabeling logic for BC use-cases where rewards and next_observations are not required. +Each function should add entries to the "task" dict. """ import tensorflow as tf +from orca.data.utils.data_utils import tree_merge + def uniform(traj: dict) -> dict: """Relabels with a true uniform distribution over future states.""" @@ -18,24 +21,9 @@ def uniform(traj: dict) -> dict: # sometimes there are floating-point errors that cause an out-of-bounds goal_idxs = tf.minimum(goal_idxs, traj_len - 1) - traj["task"] = tf.nest.map_structure( - lambda x: tf.gather(x, goal_idxs), - traj["observation"], - ) - traj["task"]["goal_timestep"] = goal_idxs - traj["task"]["end_timestep"] = tf.fill([traj_len], traj_len - 1) - - return traj - - -def no_image_conditioning(traj: dict) -> dict: - """Relabels with empty goal images.""" - traj_len = tf.shape(tf.nest.flatten(traj["observation"])[0])[0] - traj["task"] = tf.nest.map_structure( - lambda x: tf.zeros_like(x), - traj["observation"], - ) - traj["task"]["goal_timestep"] = tf.fill([traj_len], traj_len - 1) - traj["task"]["end_timestep"] = tf.fill([traj_len], traj_len - 1) + # adds keys to "task" mirroring "observation" keys (must do a tree merge to combine "pad_mask_dict" from + # "observation" and "task" properly) + goal = tf.nest.map_structure(lambda x: tf.gather(x, goal_idxs), traj["observation"]) + traj["task"] = tree_merge(traj["task"], goal) return traj diff --git a/orca/data/utils/task_augmentation.py b/orca/data/utils/task_augmentation.py index b2a5ecc6..018ce3df 100644 --- a/orca/data/utils/task_augmentation.py +++ b/orca/data/utils/task_augmentation.py @@ -2,62 +2,59 @@ Contains basic logic for randomly zero-ing out keys in the task specification. """ -from fnmatch import fnmatch -from typing import Any, Dict, List, Tuple - import tensorflow as tf from orca.data.utils.data_utils import to_padding def delete_task_conditioning( - traj: Dict[str, Any], - delete_key_groups_probs: List[Tuple[List[str], float]], + traj: dict, + keep_image_prob: float, ): """ - Randomly chooses one group, and deletes all the keys in the task dictionary matching this pattern. + Randomly drops out either the goal images or the language instruction. Only does something if both of + these are present. Args: - traj: A dictionary containing trajectory data. should have a "task" key. - delete_key_groups_probs: A list of tuples, where each tuple contains a list of patterns and their probability. + traj: A dictionary containing trajectory data. Should have a "task" key. + keep_image_prob: The probability of keeping the goal images. The probability of keeping the language + instruction is 1 - keep_image_prob. """ - if tf.math.reduce_all(traj["task"]["language_instruction"] == ""): + if "language_instruction" not in traj["task"]: + return traj + + image_keys = { + key + for key in traj["task"].keys() + if key.startswith("image_") or key.startswith("depth_") + } + if not image_keys: return traj - task = traj["task"] - new_task = task.copy() - - delete_probs = [prob for _, prob in delete_key_groups_probs] - delete_group_idx = tf.random.categorical(tf.math.log([delete_probs]), 1)[0, 0] - - image_keys = [key for key in task.keys() if "image" in key] - - for i, (delete_key_patterns, _) in enumerate(delete_key_groups_probs): - matching_keys = [ - key - for key in task.keys() - if any(fnmatch(key, pattern) for pattern in delete_key_patterns) - ] - - # When no goal images are present, the goal timestep becomes the final timestep - if all([image_key in matching_keys for image_key in image_keys]): - new_task["goal_timestep"] = tf.where( - i == delete_group_idx, - task["end_timestep"], - task["goal_timestep"], - ) - - for key in matching_keys: - new_task[key] = tf.where( - i == delete_group_idx, - to_padding(task[key]), - task[key], - ) - new_task["pad_mask_dict"][key] = tf.where( - i == delete_group_idx, - tf.zeros_like(task["pad_mask_dict"][key]), - new_task["pad_mask_dict"][key], - ) - - traj["task"] = new_task + traj_len = tf.shape(traj["action"])[0] + should_keep_images = tf.random.uniform([traj_len]) < keep_image_prob + should_keep_images |= ~traj["task"]["pad_mask_dict"]["language_instruction"] + + for key in image_keys | {"language_instruction"}: + should_keep = should_keep_images if key in image_keys else ~should_keep_images + # pad out the key + traj["task"][key] = tf.where( + should_keep, + traj["task"][key], + to_padding(traj["task"][key]), + ) + # zero out the pad mask dict for the key + traj["task"]["pad_mask_dict"][key] = tf.where( + should_keep, + traj["task"]["pad_mask_dict"][key], + tf.zeros_like(traj["task"]["pad_mask_dict"][key]), + ) + + # when no goal images are present, the goal timestep becomes the final timestep + traj["task"]["timestep"] = tf.where( + should_keep_images, + traj["task"]["timestep"], + traj_len - 1, + ) + return traj diff --git a/orca/utils/train_callbacks.py b/orca/utils/train_callbacks.py index 50f17dee..6e37c485 100644 --- a/orca/utils/train_callbacks.py +++ b/orca/utils/train_callbacks.py @@ -243,7 +243,7 @@ def eval_step(state, batch): batch["task"], self.zero_text ) if "text_conditioned" in self.modes_to_evaluate: - all_tasks["text_conditioned"] = remove_images(batch["tasks"]) + all_tasks["text_conditioned"] = remove_images(batch["task"]) if "unconditioned" in self.modes_to_evaluate: all_tasks["unconditioned"] = remove_text( diff --git a/tests/debug_config.py b/tests/debug_config.py index 854ca83d..b9904ae0 100644 --- a/tests/debug_config.py +++ b/tests/debug_config.py @@ -34,7 +34,7 @@ def get_config(): { "name": "bridge_dataset", "data_dir": "./tests/debug_dataset", - "image_obs_keys": ["image_0"], + "image_obs_keys": {"primary": "image_0"}, "state_obs_keys": ["state"], }, ], From 715282fe06b859e1892c6d6a48f8d450fb868f57 Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Sat, 9 Dec 2023 21:11:16 -0800 Subject: [PATCH 17/25] Better doc for chunk_act_obs --- orca/data/traj_transforms.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/orca/data/traj_transforms.py b/orca/data/traj_transforms.py index 4287b519..3c39f2a1 100644 --- a/orca/data/traj_transforms.py +++ b/orca/data/traj_transforms.py @@ -13,8 +13,12 @@ def chunk_act_obs( ) -> dict: """Chunks actions and observations into the given window_size. - "observation" keys are given a new axis (at index 1) of size `window_size`. "action" is given a new axis - (at index 1) of size `window_size + additional_action_window_size`. + "observation" keys are given a new axis (at index 1) of size `window_size` containing `window_size - 1` + observations from the past and the current observation. "action" is given a new axis (at index 1) of size + `window_size + additional_action_window_size` containing `window_size - 1` actions from the past, the + current action, and `additional_action_window_size` actions from the future. "pad_mask" is added to + "observation" and indicates whether an observation should be considered padding (i.e. if it would have + come from a timestep before the start of the trajectory). """ traj_len = tf.shape(traj["action"])[0] chunk_indices = tf.broadcast_to( From 838cb18bff8b8f4098e7cd21f6cccf1367488576 Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Sat, 9 Dec 2023 22:12:50 -0800 Subject: [PATCH 18/25] Refactor `get_dataset_statistics` to not depend on RLDS --- orca/data/dataset.py | 30 ++++++++++-------- orca/data/utils/data_utils.py | 58 +++++++++++++++++------------------ 2 files changed, 46 insertions(+), 42 deletions(-) diff --git a/orca/data/dataset.py b/orca/data/dataset.py index 11a6ddd0..7bef9cef 100644 --- a/orca/data/dataset.py +++ b/orca/data/dataset.py @@ -267,16 +267,6 @@ def make_dataset_from_rlds( - action # action vector - dataset_name # name of the dataset """ - builder = tfds.builder(name, data_dir=data_dir) - if "val" not in builder.info.splits: - split = "train[:95%]" if train else "train[95%:]" - else: - split = "train" if train else "val" - - dataset = dl.DLataset.from_rlds( - builder, split=split, shuffle=shuffle, num_parallel_reads=num_parallel_reads - ) - REQUIRED_KEYS = {"observation", "action"} if language_key is not None: REQUIRED_KEYS.add(language_key) @@ -339,22 +329,38 @@ def restructure(traj): "dataset_name": tf.repeat(name, traj_len), } + builder = tfds.builder(name, data_dir=data_dir) + # load or compute dataset statistics if isinstance(dataset_statistics, str): with tf.io.gfile.GFile(dataset_statistics, "r") as f: dataset_statistics = json.load(f) elif dataset_statistics is None: + full_dataset = dl.DLataset.from_rlds( + builder, split="all", shuffle=False, num_parallel_reads=num_parallel_reads + ).traj_map(restructure, num_parallel_calls) # tries to load from cache, otherwise computes on the fly dataset_statistics = get_dataset_statistics( - builder, - restructure, + full_dataset, hash_dependencies=( + str(builder.info), str(state_obs_keys), inspect.getsource(standardize_fn) if standardize_fn is not None else "", ), + save_dir=builder.data_dir, ) dataset_statistics = tree_map(np.array, dataset_statistics) + # construct the dataset + if "val" not in builder.info.splits: + split = "train[:95%]" if train else "train[95%:]" + else: + split = "train" if train else "val" + + dataset = dl.DLataset.from_rlds( + builder, split=split, shuffle=shuffle, num_parallel_reads=num_parallel_reads + ) + dataset = dataset.traj_map(restructure, num_parallel_calls) dataset = dataset.traj_map( partial( diff --git a/orca/data/utils/data_utils.py b/orca/data/utils/data_utils.py index 6af1ed64..f5706397 100644 --- a/orca/data/utils/data_utils.py +++ b/orca/data/utils/data_utils.py @@ -1,15 +1,13 @@ from enum import Enum import hashlib -import inspect import json import logging import os -from typing import Any, Callable, Dict, List, Tuple +from typing import Any, Callable, Dict, List, Optional, Tuple import dlimp as dl import numpy as np import tensorflow as tf -from tensorflow_datasets.core.dataset_builder import DatasetBuilder import tqdm @@ -79,35 +77,34 @@ def pprint_data_mixture( def get_dataset_statistics( - builder: DatasetBuilder, - restructure_fn: Callable[[dict], dict], + dataset: dl.DLataset, hash_dependencies: Tuple[str, ...], + save_dir: Optional[str] = None, ) -> dict: """Either computes the statistics of a dataset or loads them from a cache file if this function has been - called before with the same arguments. Currently, the statistics include the min/max/mean/std of the - actions and proprio as well as the number of transitions and trajectories in the dataset. + called before with the same `hash_dependencies`. Currently, the statistics include the min/max/mean/std of + the actions and proprio as well as the number of transitions and trajectories in the dataset. """ - # compute a hash of the dataset info, restructure function source code, and any additional dependencies unique_hash = hashlib.sha256( - "".join( - (str(builder.info), inspect.getsource(restructure_fn), *hash_dependencies) - ).encode(), + "".join(hash_dependencies).encode("utf-8"), usedforsecurity=False, ).hexdigest() - path = tf.io.gfile.join( - builder.info.data_dir, f"dataset_statistics_{unique_hash}.json" - ) - # fallback local path for when data_dir is not writable + + # fallback local path for when data_dir is not writable or not provided local_path = os.path.expanduser( os.path.join( "~", ".cache", "orca", - builder.name, f"dataset_statistics_{unique_hash}.json", ) ) + if save_dir is not None: + path = tf.io.gfile.join(save_dir, f"dataset_statistics_{unique_hash}.json") + else: + path = local_path + # check if cache file exists and load if tf.io.gfile.exists(path): logging.info(f"Loading existing dataset statistics from {path}.") @@ -121,21 +118,22 @@ def get_dataset_statistics( metadata = json.load(f) return metadata - dataset = ( - dl.DLataset.from_rlds(builder, split="train", shuffle=False) - .traj_map(restructure_fn) - .traj_map( - lambda traj: { - "action": traj["action"], - "proprio": traj["observation"]["proprio"] - if "proprio" in traj["observation"] - else tf.zeros_like(traj["action"]), - } - ) + dataset = dataset.traj_map( + lambda traj: { + "action": traj["action"], + "proprio": traj["observation"]["proprio"] + if "proprio" in traj["observation"] + else tf.zeros_like(traj["action"]), + } ) + + cardinality = dataset.cardinality().numpy() + if cardinality == tf.data.INFINITE_CARDINALITY: + raise ValueError("Cannot compute dataset statistics for infinite datasets.") + logging.info( - f"Computing dataset statistics for {builder.name}. This may take awhile, " - "but should only need to happen once." + "Computing dataset statistics. This may take awhile, but should only need to happen " + "once for each dataset." ) actions = [] proprios = [] @@ -143,7 +141,7 @@ def get_dataset_statistics( num_trajectories = 0 for traj in tqdm.tqdm( dataset.iterator(), - total=builder.info.splits["train"].num_examples, + total=cardinality if cardinality != tf.data.UNKNOWN_CARDINALITY else None, ): actions.append(traj["action"]) proprios.append(traj["proprio"]) From 8f83e85211863d448c7bd2aa79c7a2f6094b820e Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Sat, 9 Dec 2023 22:24:17 -0800 Subject: [PATCH 19/25] Add back zeroing after goal --- orca/data/dataset.py | 22 +++++++++++++++++++++- orca/data/oxe/__init__.py | 3 +++ orca/data/traj_transforms.py | 27 ++++++++++++++++++++++----- 3 files changed, 46 insertions(+), 6 deletions(-) diff --git a/orca/data/dataset.py b/orca/data/dataset.py index 7bef9cef..055c16b9 100644 --- a/orca/data/dataset.py +++ b/orca/data/dataset.py @@ -204,6 +204,7 @@ def make_dataset_from_rlds( language_key: Optional[str] = None, action_proprio_normalization_type: NormalizationType = NormalizationType.NORMAL, dataset_statistics: Optional[Union[dict, str]] = None, + absolute_action_mask: Optional[Sequence[bool]] = None, num_parallel_reads: int = tf.data.AUTOTUNE, num_parallel_calls: int = tf.data.AUTOTUNE, ) -> Tuple[dl.DLataset, dict]: @@ -253,6 +254,12 @@ def make_dataset_from_rlds( "std" keys. If `action_proprio_normalization_type` is "bounds", this should contain "min" and "max" keys. May also provide "num_transitions" and "num_trajectories" keys for downstream usage (e.g., for `make_interleaved_dataset`). If not provided, the statistics will be computed on the fly. + absolute_action_mask (Sequence[bool], optional): By default, all action dimensions are assumed to be + relative. This is important for when `additional_action_window_size > 0`: actions that are taken + from beyond the end of the trajectory (or beyond the goal timestep when goal relabeling is used) + need to be made "neutral" to indicate that the task has been completed. For relative actions, + "neutral" means zero, but for absolute actions, "neutral" means repeating the last valid action. + This mask, if provided, indicates which action dimensions are absolute. num_parallel_reads (int): number of parallel read workers. Default to AUTOTUNE. num_parallel_calls (int): number of parallel calls for traj_map operations. Default to AUTOTUNE. Returns: @@ -322,13 +329,26 @@ def restructure(traj): ) task["language_instruction"] = traj.pop(language_key) - return { + traj = { "observation": new_obs, "task": task, "action": tf.cast(traj["action"], tf.float32), "dataset_name": tf.repeat(name, traj_len), } + if absolute_action_mask is not None: + if len(absolute_action_mask) != traj["action"].shape[-1]: + raise ValueError( + f"Length of absolute_action_mask ({len(absolute_action_mask)}) " + f"does not match action dimension ({traj['action'].shape[-1]})." + ) + traj["absolute_action_mask"] = tf.tile( + tf.convert_to_tensor(absolute_action_mask, dtype=tf.bool)[None], + [traj_len, 1], + ) + + return traj + builder = tfds.builder(name, data_dir=data_dir) # load or compute dataset statistics diff --git a/orca/data/oxe/__init__.py b/orca/data/oxe/__init__.py index 29f622ac..85b75f12 100755 --- a/orca/data/oxe/__init__.py +++ b/orca/data/oxe/__init__.py @@ -53,6 +53,9 @@ def make_oxe_dataset_kwargs_and_weights( ) continue + # with EEF_POS actions, only the last action dimension is absolute + dataset_kwargs["absolute_action_mask"] = [False] * 6 + [True] + # adjust loaded features in kwargs dataset_kwargs["image_obs_keys"] = { k: v diff --git a/orca/data/traj_transforms.py b/orca/data/traj_transforms.py index 3c39f2a1..716d2726 100644 --- a/orca/data/traj_transforms.py +++ b/orca/data/traj_transforms.py @@ -3,6 +3,8 @@ that represents a single trajectory, meaning each tensor has the same leading dimension (the trajectory length). """ +import logging + import tensorflow as tf @@ -21,6 +23,7 @@ def chunk_act_obs( come from a timestep before the start of the trajectory). """ traj_len = tf.shape(traj["action"])[0] + action_dim = traj["action"].shape[-1] chunk_indices = tf.broadcast_to( tf.range(-window_size + 1, 1), [traj_len, window_size] ) + tf.broadcast_to(tf.range(traj_len)[:, None], [traj_len, window_size]) @@ -52,12 +55,26 @@ def chunk_act_obs( # indicates whether an entire observation is padding traj["observation"]["pad_mask"] = chunk_indices >= 0 - # Actions past the goal timestep become no-ops + # if no absolute_action_mask was provided, assume all actions are relative + if "absolute_action_mask" not in traj and additional_action_window_size > 0: + logging.warning( + "additional_action_window_size > 0 but no absolute_action_mask was provided. " + "Assuming all actions are relative for the purpose of making neutral actions." + ) + absolute_action_mask = traj.get( + "absolute_action_mask", tf.zeros([traj_len, action_dim], dtype=tf.bool) + ) + neutral_actions = tf.where( + absolute_action_mask[:, None, :], + traj["action"], # absolute actions are repeated (already done during chunking) + tf.zeros_like(traj["action"]), # relative actions are zeroed + ) + + # actions past the goal timestep become neutral action_past_goal = action_chunk_indices > goal_timestep[:, None] - # zero_actions = make_neutral_actions(traj["action"], action_encoding) - # traj["action"] = tf.where( - # action_past_goal[:, :, None], zero_actions, traj["action"] - # ) + traj["action"] = tf.where( + action_past_goal[:, :, None], neutral_actions, traj["action"] + ) return traj From bc9773c300e0345f768a0a04b571e672f8e1c9d4 Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Sun, 10 Dec 2023 17:43:42 -0800 Subject: [PATCH 20/25] Refactoring, better docs --- config.py | 2 +- orca/data/dataset.py | 87 +++++++++++++++------------ orca/data/obs_transforms.py | 13 +++- orca/data/traj_transforms.py | 20 +++--- orca/data/utils/data_utils.py | 9 ++- orca/model/components/action_heads.py | 4 +- 6 files changed, 77 insertions(+), 58 deletions(-) diff --git a/config.py b/config.py index 76d59765..b3d051b0 100644 --- a/config.py +++ b/config.py @@ -133,7 +133,7 @@ def get_dataset_config(modality="multimodal", window_size=1): ), "traj_transform_kwargs": dict( window_size=window_size, - additional_action_window_size=0, + future_action_window_size=0, goal_relabeling_strategy="uniform", subsample_length=100, **task_augmentation, diff --git a/orca/data/dataset.py b/orca/data/dataset.py index 055c16b9..5121d196 100644 --- a/orca/data/dataset.py +++ b/orca/data/dataset.py @@ -1,7 +1,7 @@ from functools import partial import inspect import json -from typing import Callable, List, Mapping, Optional, Sequence, Tuple, Union +from typing import Callable, Mapping, Optional, Sequence, Tuple, Union from absl import logging import dlimp as dl @@ -28,7 +28,7 @@ def apply_trajectory_transforms( goal_relabeling_strategy: Optional[str] = None, goal_relabeling_kwargs: dict = {}, window_size: int = 1, - additional_action_window_size: int = 0, + future_action_window_size: int = 0, subsample_length: Optional[int] = None, skip_unlabeled: bool = False, max_action: Optional[float] = None, @@ -52,10 +52,10 @@ def apply_trajectory_transforms( no goal relabeling. See `goal_relabeling.py`. goal_relabeling_kwargs (dict, optional): Additional keyword arguments to pass to the goal relabeling function. window_size (int, optional): The length of the snippets that trajectories are chunked into. - additional_action_window_size (int, optional): The number of additional actions beyond window_size to include + future_action_window_size (int, optional): The number of future actions beyond window_size to include in the chunked actions. - subsample_length (int, optional): If provided, trajectories longer than this will be - subsampled to this length (after goal relabeling). + subsample_length (int, optional): If provided, trajectories longer than this will be subsampled to + this length (after goal relabeling and chunking). skip_unlabeled (bool, optional): Whether to skip trajectories with no language labels. max_action: (float, optional): If provided, trajectories in which *any* action dimension of *any* transition has an absolute value larger than this will be skipped. @@ -67,7 +67,11 @@ def apply_trajectory_transforms( function. num_parallel_calls (int, optional): number of parallel calls for map operations. Default to AUTOTUNE. """ - if skip_unlabeled and "language_instruction" in dataset.element_spec["task"]: + if skip_unlabeled: + if "language_instruction" not in dataset.element_spec["task"]: + raise ValueError( + "skip_unlabeled=True but dataset does not have language labels." + ) dataset = dataset.filter( lambda x: tf.math.reduce_any(x["task"]["language_instruction"] != "") ) @@ -108,12 +112,13 @@ def apply_trajectory_transforms( num_parallel_calls, ) - # chunks actions and observations + # chunks observations and actions, giving them a new axis at index 1 of size `window_size` and + # `window_size + future_action_window_size`, respectively dataset = dataset.traj_map( partial( traj_transforms.chunk_act_obs, window_size=window_size, - additional_action_window_size=additional_action_window_size, + future_action_window_size=future_action_window_size, ), num_parallel_calls, ) @@ -158,7 +163,7 @@ def apply_frame_transforms( # convenience wrapper that takes a function that operates on a non-chunked "observation" dict and applies # it to the chunked "observation" dict as well as the non-chunked "task" dict - def apply_obs_transform(fn: Callable[[dict], dict], frame): + def apply_obs_transform(fn: Callable[[dict], dict], frame: dict) -> dict: # task is not chunked -- apply fn directly frame["task"] = fn(frame["task"]) # observation is chunked -- apply fn along first axis @@ -180,7 +185,7 @@ def apply_obs_transform(fn: Callable[[dict], dict], frame): if train: # augment all images with the same seed, skipping padding images - def aug(frame): + def aug(frame: dict): seed = tf.random.uniform([2], maxval=tf.dtypes.int32.max, dtype=tf.int32) aug_fn = partial( obs_transforms.augment, seed=seed, augment_kwargs=image_augment_kwargs @@ -195,6 +200,7 @@ def aug(frame): def make_dataset_from_rlds( name: str, data_dir: str, + *, train: bool, standardize_fn: Optional[Callable[[dict], dict]] = None, shuffle: bool = True, @@ -233,9 +239,11 @@ def make_dataset_from_rlds( Args: name (str): The name of the RLDS dataset (usually "name" or "name:version"). data_dir (str): The path to the data directory. - train (bool): Whether to use the training or validation set. - shuffle (bool, optional): Whether to shuffle the file read order (does NOT fully shuffle directories, + train (bool): Whether to use the training or validation split. + shuffle (bool, optional): Whether to shuffle the file read order (does NOT fully shuffle the dataset, since one file usually contains many trajectories!). + standardize_fn (Callable[[dict], dict], optional): A function that, if provided, will be the first + thing applied to each trajectory. image_obs_keys (Mapping[str, str|None]): Mapping from {new: old} indicating which RGB images to extract from the "observation" dict. `new_obs = {f"image_{new}": old_obs[old] for new, old in image_obs_keys.items()}`. If a value of `old` is None, inserts a padding image instead (empty @@ -255,7 +263,7 @@ def make_dataset_from_rlds( keys. May also provide "num_transitions" and "num_trajectories" keys for downstream usage (e.g., for `make_interleaved_dataset`). If not provided, the statistics will be computed on the fly. absolute_action_mask (Sequence[bool], optional): By default, all action dimensions are assumed to be - relative. This is important for when `additional_action_window_size > 0`: actions that are taken + relative. This is important for when `future_action_window_size > 0`: actions that are taken from beyond the end of the trajectory (or beyond the goal timestep when goal relabeling is used) need to be made "neutral" to indicate that the task has been completed. For relative actions, "neutral" means zero, but for absolute actions, "neutral" means repeating the last valid action. @@ -396,17 +404,18 @@ def restructure(traj): def make_single_dataset( dataset_kwargs: dict, - traj_transform_kwargs: dict, - frame_transform_kwargs: dict, + *, train: bool, + traj_transform_kwargs: dict = {}, + frame_transform_kwargs: dict = {}, ) -> dl.DLataset: """Creates a single dataset from kwargs. Returns a dataset of trajectories. Args: dataset_kwargs: kwargs passed to `make_dataset_from_rlds` that are dataset-specific. + train: whether this is a training or validation dataset. traj_transform_kwargs: kwargs passed to 'apply_trajectory_transforms'. frame_transform_kwargs: kwargs passed to 'get_frame_transforms'. - train: whether this is a training or validation dataset. """ dataset, dataset_statistics = make_dataset_from_rlds( **dataset_kwargs, @@ -424,41 +433,47 @@ def make_single_dataset( def make_interleaved_dataset( - *, dataset_kwargs_list: Sequence[dict], - traj_transform_kwargs: dict, - frame_transform_kwargs: dict, + sample_weights: Optional[Sequence[float]] = None, + *, train: bool, - sample_weights: Optional[List[float]], - balance_weights: bool, shuffle_buffer_size: int, batch_size: int, - traj_transform_threads: Optional[int], - traj_read_threads: Optional[int], + traj_transform_kwargs: dict = {}, + frame_transform_kwargs: dict = {}, + balance_weights: bool = False, + traj_transform_threads: Optional[int] = None, + traj_read_threads: Optional[int] = None, ) -> dl.DLataset: """Creates an interleaved dataset from list of dataset kwargs. Returns a dataset of batched frames. Args: dataset_kwargs_list: list of kwargs, each element of which is passed to `make_dataset_from_rlds`. - "num_parallel_calls" and "num_parallel_reads" are ignored. - traj_transform_kwargs: kwargs passed to 'apply_trajectory_transforms'. "num_parallel_calls" is ignored. - frame_transform_kwargs: kwargs passed to 'get_frame_transforms'. - train: whether this is a training or validation dataset. + "num_parallel_calls" and "num_parallel_reads" are overidden using `traj_transform_threads` and + `traj_read_threads`, respectively. sample_weights: sampling weights for each dataset in list. If None, defaults to uniform. + train: whether this is a training or validation dataset. + shuffle_buffer_size: size of the dataset shuffle buffer (in number of frames). + batch_size: batch size. + traj_transform_kwargs: kwargs passed to `apply_trajectory_transforms`. "num_parallel_calls" is + overidden using `traj_transform_threads`. + frame_transform_kwargs: kwargs passed to `apply_frame_transforms`. balance_weights: if True, the sample weights are multiplied by the number of frames in each dataset. This makes it so that, if all the sample weights are equal, one full iteration through the interleaved dataset will correspond to one full iteration through each individual dataset (only in expectation, since in practice the sampling is random). - shuffle_buffer_size: size of the dataset shuffle buffer (in number of frames). - batch_size: batch size. traj_transform_threads: total number of parallel calls for trajectory transforms, distributed across datasets according to their sampling weights. If None, defaults to AUTOTUNE for every dataset. traj_read_threads: total number of parallel read workers for trajectory transforms, distributed across datasets according to their sampling weights. If None, defaults to AUTOTUNE for every dataset. """ + # default to uniform sampling if not sample_weights: sample_weights = [1.0] * len(dataset_kwargs_list) - assert len(sample_weights) == len(dataset_kwargs_list) + if len(sample_weights) != len(dataset_kwargs_list): + raise ValueError( + f"sample_weights must be None or have length {len(dataset_kwargs_list)}." + ) # go through datasets once to get sizes dataset_sizes = [] @@ -475,14 +490,8 @@ def make_interleaved_dataset( pprint_data_mixture(dataset_kwargs_list, sample_weights) # allocate threads based on weights - if traj_transform_threads is None: - threads_per_dataset = [tf.data.AUTOTUNE] * len(dataset_kwargs_list) - else: - threads_per_dataset = allocate_threads(traj_transform_threads, sample_weights) - if traj_read_threads is None: - reads_per_dataset = [tf.data.AUTOTUNE] * len(dataset_kwargs_list) - else: - reads_per_dataset = allocate_threads(traj_read_threads, sample_weights) + threads_per_dataset = allocate_threads(traj_transform_threads, sample_weights) + reads_per_dataset = allocate_threads(traj_read_threads, sample_weights) logging.info("Threads per dataset: %s", threads_per_dataset) logging.info("Reads per dataset: %s", reads_per_dataset) @@ -510,7 +519,7 @@ def make_interleaved_dataset( ).flatten(num_parallel_calls=threads) datasets.append(dataset) - # interleave at the transition level and then shuffle + # interleave at the frame level and then shuffle dataset: dl.DLataset = dl.DLataset.sample_from_datasets( datasets, sample_weights ).shuffle(shuffle_buffer_size) diff --git a/orca/data/obs_transforms.py b/orca/data/obs_transforms.py index 07364307..bcdd9d19 100644 --- a/orca/data/obs_transforms.py +++ b/orca/data/obs_transforms.py @@ -3,12 +3,15 @@ "observation" dictionary, and are applied at a per-frame level. """ import logging +from typing import Mapping, Tuple, Union import dlimp as dl import tensorflow as tf -def augment(obs: dict, seed, augment_kwargs) -> dict: +def augment( + obs: dict, seed: tf.Tensor, augment_kwargs: Union[dict, Mapping[str, dict]] +) -> dict: """Augments images, skipping padding images.""" image_names = {key[6:] for key in obs if key.startswith("image_")} @@ -30,13 +33,17 @@ def augment(obs: dict, seed, augment_kwargs) -> dict: **kwargs, seed=seed + i, # augment each image differently ), - lambda: obs[f"image_{name}"], + lambda: obs[f"image_{name}"], # skip padding images ) return obs -def decode_and_resize(obs: dict, resize_size, depth_resize_size) -> dict: +def decode_and_resize( + obs: dict, + resize_size: Union[Tuple[int, int], Mapping[str, Tuple[int, int]]], + depth_resize_size: Union[Tuple[int, int], Mapping[str, Tuple[int, int]]], +) -> dict: """Decodes images and depth images, and then optionally resizes them.""" # just gets the part after "image_" or "depth_" image_names = {key[6:] for key in obs if key.startswith("image_")} diff --git a/orca/data/traj_transforms.py b/orca/data/traj_transforms.py index 716d2726..010777ca 100644 --- a/orca/data/traj_transforms.py +++ b/orca/data/traj_transforms.py @@ -11,16 +11,16 @@ def chunk_act_obs( traj: dict, window_size: int, - additional_action_window_size: int = 0, + future_action_window_size: int = 0, ) -> dict: """Chunks actions and observations into the given window_size. "observation" keys are given a new axis (at index 1) of size `window_size` containing `window_size - 1` observations from the past and the current observation. "action" is given a new axis (at index 1) of size - `window_size + additional_action_window_size` containing `window_size - 1` actions from the past, the - current action, and `additional_action_window_size` actions from the future. "pad_mask" is added to - "observation" and indicates whether an observation should be considered padding (i.e. if it would have - come from a timestep before the start of the trajectory). + `window_size + future_action_window_size` containing `window_size - 1` actions from the past, the current + action, and `future_action_window_size` actions from the future. "pad_mask" is added to "observation" and + indicates whether an observation should be considered padding (i.e. if it would have come from a timestep + before the start of the trajectory). """ traj_len = tf.shape(traj["action"])[0] action_dim = traj["action"].shape[-1] @@ -29,11 +29,11 @@ def chunk_act_obs( ) + tf.broadcast_to(tf.range(traj_len)[:, None], [traj_len, window_size]) action_chunk_indices = tf.broadcast_to( - tf.range(-window_size + 1, 1 + additional_action_window_size), - [traj_len, window_size + additional_action_window_size], + tf.range(-window_size + 1, 1 + future_action_window_size), + [traj_len, window_size + future_action_window_size], ) + tf.broadcast_to( tf.range(traj_len)[:, None], - [traj_len, window_size + additional_action_window_size], + [traj_len, window_size + future_action_window_size], ) floored_chunk_indices = tf.maximum(chunk_indices, 0) @@ -56,9 +56,9 @@ def chunk_act_obs( traj["observation"]["pad_mask"] = chunk_indices >= 0 # if no absolute_action_mask was provided, assume all actions are relative - if "absolute_action_mask" not in traj and additional_action_window_size > 0: + if "absolute_action_mask" not in traj and future_action_window_size > 0: logging.warning( - "additional_action_window_size > 0 but no absolute_action_mask was provided. " + "future_action_window_size > 0 but no absolute_action_mask was provided. " "Assuming all actions are relative for the purpose of making neutral actions." ) absolute_action_mask = traj.get( diff --git a/orca/data/utils/data_utils.py b/orca/data/utils/data_utils.py index f5706397..62be05d7 100644 --- a/orca/data/utils/data_utils.py +++ b/orca/data/utils/data_utils.py @@ -274,10 +274,13 @@ def invert_gripper_actions(actions: tf.Tensor): return 1 - actions -def allocate_threads(n: int, weights: np.ndarray): - """Allocates an integer n across an array based on weights. The final array sums to n, but each element is - no less than 1. +def allocate_threads(n: Optional[int], weights: np.ndarray): + """Allocates an integer number of threads across datasets based on weights. The final array sums to `n`, + but each element is no less than 1. If `n` is None, then every dataset is assigned a value of AUTOTUNE. """ + if n is None: + return np.array([tf.data.AUTOTUNE] * len(weights)) + assert np.all(weights >= 0), "Weights must be non-negative" assert ( len(weights) <= n diff --git a/orca/model/components/action_heads.py b/orca/model/components/action_heads.py index fa28a8d6..5fbf8bca 100644 --- a/orca/model/components/action_heads.py +++ b/orca/model/components/action_heads.py @@ -111,7 +111,7 @@ def loss( To predict actions for horizon {horizon} and future prediction horizon {self.pred_horizon}, the ground-truth actions must have at least {horizon + self.pred_horizon - 1} timesteps, but got shape {actions.shape}. - Did you make sure to set "additional_action_window_size" correctly in the data config? + Did you make sure to set "future_action_window_size" correctly in the data config? """ # compute log probabilities for predicted actions @@ -317,7 +317,7 @@ def loss( To predict actions for horizon {horizon} and future prediction horizon {self.pred_horizon}, the ground-truth actions must have at least {horizon + self.pred_horizon - 1} timesteps, but got shape {actions.shape}. - Did you make sure to set "additional_action_window_size" correctly in the data config? + Did you make sure to set "future_action_window_size" correctly in the data config? """ # chunk the target actions to match the predicted actions # only use first horizon timesteps from the window From 16c04d4e9cd1bbd59bf6fec07be90ab43a2efe1d Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Sun, 10 Dec 2023 18:24:11 -0800 Subject: [PATCH 21/25] Refactor to make getting oxe dataset kwargs easier --- orca/data/obs_transforms.py | 2 +- orca/data/oxe/__init__.py | 157 ++++++++++++++++++++++-------------- 2 files changed, 98 insertions(+), 61 deletions(-) diff --git a/orca/data/obs_transforms.py b/orca/data/obs_transforms.py index bcdd9d19..14530498 100644 --- a/orca/data/obs_transforms.py +++ b/orca/data/obs_transforms.py @@ -2,9 +2,9 @@ Contains observation-level transforms used in the orca data pipeline. These transforms operate on the "observation" dictionary, and are applied at a per-frame level. """ -import logging from typing import Mapping, Tuple, Union +from absl import logging import dlimp as dl import tensorflow as tf diff --git a/orca/data/oxe/__init__.py b/orca/data/oxe/__init__.py index 85b75f12..ee61e528 100755 --- a/orca/data/oxe/__init__.py +++ b/orca/data/oxe/__init__.py @@ -5,87 +5,124 @@ from orca.data.oxe.oxe_dataset_configs import ActionEncoding, OXE_DATASET_CONFIGS from orca.data.oxe.oxe_dataset_mixes import OXE_NAMED_MIXES from orca.data.oxe.oxe_standardization_transforms import OXE_STANDARDIZATION_TRANSFORMS +from orca.data.utils.data_utils import NormalizationType + + +def make_oxe_dataset_kwargs( + name: str, + data_dir: str, + load_camera_views: Sequence[str] = ("primary",), + load_depth: bool = False, + load_proprio: bool = True, + load_language: bool = True, + action_proprio_normalization_type: NormalizationType = NormalizationType.NORMAL, +) -> Dict[str, Any]: + """Generates dataset kwargs for a given dataset from Open X-Embodiment. The returned kwargs can be passed + directly into `orca.data.dataset.make_dataset_from_rlds`. + + Args: + name: Name of the dataset to load. See `oxe_dataset_configs.py` for available datasets. + data_dir: Base data directory that contains the dataset. + load_camera_views: Which views to load. See `oxe_dataset_configs.py` for available views. + load_depth: If True, loads corresponding depth channels for each RGB channel. + load_proprio: If True, loads proprioceptive information. + load_language: If True, loads language instructions. + action_proprio_normalization_type: Normalization type to use for proprioceptive actions. + """ + dataset_kwargs = copy.deepcopy(OXE_DATASET_CONFIGS[name]) + if dataset_kwargs["action_encoding"] is not ActionEncoding.EEF_POS: + raise ValueError( + f"Cannot load {name} since only EEF pose delta action encoding is supported." + ) + + # with EEF_POS actions, only the last action dimension is absolute + dataset_kwargs["absolute_action_mask"] = [False] * 6 + [True] + + # adjust loaded camera views + dataset_kwargs["image_obs_keys"] = { + k: v + for k, v in dataset_kwargs["image_obs_keys"].items() + if k in load_camera_views + } + dataset_kwargs["depth_obs_keys"] = { + k: v + for k, v in dataset_kwargs["depth_obs_keys"].items() + if k in load_camera_views + } + + if not load_depth: + dataset_kwargs.pop("depth_obs_keys") + if not load_proprio: + dataset_kwargs.pop("state_obs_keys") + + if load_language: + dataset_kwargs["language_key"] = "language_instruction" + + dataset_kwargs[ + "action_proprio_normalization_type" + ] = action_proprio_normalization_type + + del dataset_kwargs["state_encoding"] + del dataset_kwargs["action_encoding"] + + dataset_kwargs["standardize_fn"] = OXE_STANDARDIZATION_TRANSFORMS[name] + + return {"name": name, "data_dir": data_dir, **dataset_kwargs} def make_oxe_dataset_kwargs_and_weights( data_mix: Union[str, Sequence[Tuple[str, float]]], data_dir: str, - deduplicate: bool = True, load_camera_views: Sequence[str] = ("primary",), - load_depth: bool = True, + load_depth: bool = False, load_proprio: bool = True, + load_language: bool = True, + action_proprio_normalization_type: NormalizationType = NormalizationType.NORMAL, ) -> Tuple[Dict[str, Any], List[float]]: """ - Generates dataset kwargs for a given dataset mix from the Open X-Embodiment dataset. + Generates dataset kwargs for a given dataset mix from the Open X-Embodiment dataset. The returned kwargs + and weights can be passed directly into `orca.data.dataset.make_interleaved_dataset`. Args: - data_mix: List of (dataset name, sampling weight) tuples, or a string specifying a pre-defined mix to + data_mix: List of (dataset name, sampling weight) tuples, or a string specifying a pre-defined mix to load from `OXE_NAMED_MIXES`. - data_dir: Base data directory that gets registered in each dataset. - deduplicate: If True, discards any duplicate dataset entries based on dataset name. - load_camera_views: Which views to load from each dataset. See the top of `oxe_dataset_configs.py` - for available views. - load_depth: If True, loads corresponding depth channels for each RGB channel. - load_proprio: If True, loads proprioceptive information. + data_dir: Base data directory that contains the datasets. + load_camera_views: Which views to load. See `oxe_dataset_configs.py` for available views. + load_depth: If True, loads corresponding depth channels for each RGB channel. + load_proprio: If True, loads proprioceptive information. + load_language: If True, loads language instructions. + action_proprio_normalization_type: Normalization type to use for proprioceptive actions. Returns: Tuple of (dataset_kwargs_list, sampling weights). """ if isinstance(data_mix, str): data_mix = OXE_NAMED_MIXES[data_mix] - if deduplicate: - filtered_datasets, included_dataset_names = [], [] - for name, weight in data_mix: - if name not in included_dataset_names: - filtered_datasets.append((name, weight)) - included_dataset_names.append(name) - else: - logging.warning(f"Skipping duplicate: {(name, weight)}.") - data_mix = filtered_datasets + filtered_datasets, included_dataset_names = [], [] + for name, weight in data_mix: + if name not in included_dataset_names: + filtered_datasets.append((name, weight)) + included_dataset_names.append(name) + else: + logging.warning(f"Skipping duplicate: {(name, weight)}.") + data_mix = filtered_datasets data_kwargs_list, weights = [], [] for name, weight in data_mix: - dataset_kwargs = copy.deepcopy(OXE_DATASET_CONFIGS[name]) - if dataset_kwargs["action_encoding"] is not ActionEncoding.EEF_POS: - logging.warning( - f"Skipping {name} since only EEF pose delta action encoding " - f"is supported." + try: + data_kwargs_list.append( + make_oxe_dataset_kwargs( + name, + data_dir, + load_camera_views, + load_depth, + load_proprio, + load_language, + action_proprio_normalization_type, + ) ) - continue - - # with EEF_POS actions, only the last action dimension is absolute - dataset_kwargs["absolute_action_mask"] = [False] * 6 + [True] - - # adjust loaded features in kwargs - dataset_kwargs["image_obs_keys"] = { - k: v - for k, v in dataset_kwargs["image_obs_keys"].items() - if k in load_camera_views - } - - if not any([e is not None for e in dataset_kwargs["image_obs_keys"].values()]): - logging.warning(f"Skipping {name} since no image input was loaded from it.") - continue - - dataset_kwargs["depth_obs_keys"] = { - k: v - for k, v in dataset_kwargs["depth_obs_keys"].items() - if k in load_camera_views - } - - if not load_depth: - dataset_kwargs.pop("depth_obs_keys") - if not load_proprio: - dataset_kwargs.pop("state_obs_keys") - - del dataset_kwargs["state_encoding"] - del dataset_kwargs["action_encoding"] - - # get standardization transform - dataset_kwargs["standardize_fn"] = OXE_STANDARDIZATION_TRANSFORMS[name] - - # add dataset to list - data_kwargs_list.append({"name": name, "data_dir": data_dir, **dataset_kwargs}) - weights.append(weight) + weights.append(weight) + except ValueError as e: + logging.warning(f"Skipping {name} due to error: {e}") return data_kwargs_list, weights From 21e1c39e7d7ed674bcec82104b29eff331df8cd4 Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Sun, 10 Dec 2023 19:49:35 -0800 Subject: [PATCH 22/25] Add missing camera views warning --- orca/data/oxe/__init__.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/orca/data/oxe/__init__.py b/orca/data/oxe/__init__.py index ee61e528..aafc39cc 100755 --- a/orca/data/oxe/__init__.py +++ b/orca/data/oxe/__init__.py @@ -39,6 +39,10 @@ def make_oxe_dataset_kwargs( dataset_kwargs["absolute_action_mask"] = [False] * 6 + [True] # adjust loaded camera views + if missing_keys := (set(load_camera_views) - set(dataset_kwargs["image_obs_keys"])): + raise ValueError( + f"Cannot load {name} with views {missing_keys} since they are not available." + ) dataset_kwargs["image_obs_keys"] = { k: v for k, v in dataset_kwargs["image_obs_keys"].items() From 1b236d85c6140973551d9b3e29077c782223fe85 Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Sun, 10 Dec 2023 20:53:32 -0800 Subject: [PATCH 23/25] Add dataloading example --- .pre-commit-config.yaml | 1 + examples/dataloading.ipynb | 528 +++++++++++++++++++++++++++++++++++++ 2 files changed, 529 insertions(+) create mode 100644 examples/dataloading.ipynb diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 4b6a045e..049912dc 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,6 +7,7 @@ repos: - id: check-yaml - id: check-ast - id: check-added-large-files + exclude: ^examples/ - id: check-case-conflict - id: check-merge-conflict - id: end-of-file-fixer diff --git a/examples/dataloading.ipynb b/examples/dataloading.ipynb new file mode 100644 index 00000000..e02646d1 --- /dev/null +++ b/examples/dataloading.ipynb @@ -0,0 +1,528 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ORCA Dataloading Examples\n", + "\n", + "This notebook will walk you through some of the primary features of the ORCA dataloader. Data is, after all, the most important part of any machine learning pipeline!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading Open X-Embodiment Data\n", + "\n", + "The [Open X-Embodiment (OXE)](https://robotics-transformer-x.github.io/) project was a massive cross-instutition data collection collaboration the likes of which robot learning has never seen before. The resulting dataset includes 22 different robots demonstrating 527 skills and totals over 1 million trajectories. However, as we found throughout the course of the ORCA project, simply loading such a diverse set of robot data is no small feat. We hope that the `orca.data` pipeline can help kickstart anyone who hopes to take advantage of the massive collection of robot demonstrations that is OXE!\n", + "\n", + "### Minimum working example to load a single OXE dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-10 18:52:50.886615: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 18:52:52.706103: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "WARNING:absl:No resize_size was provided for image_primary. This will result in 1x1 padding images, which may cause errors if you mix padding and non-padding images.\n" + ] + } + ], + "source": [ + "# minimum working example to load a single OXE dataset\n", + "from orca.data.oxe import make_oxe_dataset_kwargs\n", + "from orca.data.dataset import make_single_dataset\n", + "\n", + "dataset_kwargs = make_oxe_dataset_kwargs(\n", + " # see orca/data/oxe/oxe_dataset_configs.py for available datasets\n", + " \"bridge_dataset\",\n", + " # can be local or on cloud storage (anything supported by TFDS)\n", + " # \"/path/to/base/oxe/directory\",\n", + " \"gs://rail-orca-central2/resize_256_256\",\n", + ")\n", + "dataset = make_single_dataset(dataset_kwargs, train=True) # load the train split\n", + "iterator = dataset.iterator()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top-level keys: dict_keys(['observation', 'task', 'action', 'dataset_name', 'absolute_action_mask'])\n", + "Observation keys: dict_keys(['image_primary', 'proprio', 'timestep', 'pad_mask_dict', 'pad_mask'])\n", + "Task keys: dict_keys(['language_instruction', 'pad_mask_dict'])\n" + ] + } + ], + "source": [ + "# make_single_dataset yields entire trajectories\n", + "traj = next(iterator)\n", + "print(\"Top-level keys: \", traj.keys())\n", + "print(\"Observation keys: \", traj[\"observation\"].keys())\n", + "print(\"Task keys: \", traj[\"task\"].keys())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(48, 1, 256, 256, 3)\n" + ] + }, + { + "data": { + "image/jpeg": 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", 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", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from PIL import Image\n", + "import numpy as np\n", + "\n", + "traj = next(iterator)\n", + "images = traj[\"observation\"][\"image_primary\"]\n", + "# should be: (traj_len, window_size, height, width, channels)\n", + "# (window_size defaults to 1)\n", + "print(images.shape) \n", + "Image.fromarray(np.concatenate(images.squeeze()[:5], axis=1))\n", + "# run this cell muliple times to see different trajectories!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-10 18:53:08.120280: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:422] ShuffleDatasetV3:602: Filling up shuffle buffer (this may take a while): 1 of 1000\n", + "2023-12-10 18:53:08.815212: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:452] Shuffle buffer filled.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(64, 1, 256, 256, 3)\n" + ] + }, + { + "data": { + "image/jpeg": 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", 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", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# you should set these much higher in practice (as large as your memory can hold!)\n", + "SHUFFLE_BUFFER_SIZE = 1000\n", + "BATCH_SIZE = 64\n", + "\n", + "# turning a dataset of trajectories into a training-ready batched dataset\n", + "train_dataset = (\n", + " dataset.flatten() # flattens trajectories into individual frames\n", + " .shuffle(SHUFFLE_BUFFER_SIZE) # shuffles the frames\n", + " .batch(BATCH_SIZE) # batches the frames\n", + ")\n", + "batch = next(train_dataset.iterator())\n", + "images = batch[\"observation\"][\"image_primary\"]\n", + "# should be: (batch_size, window_size, height, width, channels)\n", + "print(images.shape)\n", + "Image.fromarray(np.concatenate(images.squeeze()[:5], axis=1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading a training-ready OXE mix\n", + "\n", + "In reality, you're probably going to want to mix multiple datasets together, as well as use other transformations such as resizing, augmentation, windowing, etc. This section will show you how to get a proper OXE mix up and running, as well as demonstrate additional `orca.data` features for more realistic use-cases." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'name': 'fractal20220817_data',\n", + " 'data_dir': 'gs://rail-orca-central2/resize_256_256',\n", + " 'image_obs_keys': {'primary': 'image', 'wrist': None},\n", + " 'state_obs_keys': ['base_pose_tool_reached', 'gripper_closed'],\n", + " 'absolute_action_mask': [False, False, False, False, False, False, True],\n", + " 'language_key': 'language_instruction',\n", + " 'action_proprio_normalization_type': ,\n", + " 'standardize_fn': Dict[str, Any]>}" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from orca.data.oxe import make_oxe_dataset_kwargs_and_weights\n", + "from orca.data.dataset import make_interleaved_dataset\n", + "\n", + "dataset_kwargs_list, sample_weights = make_oxe_dataset_kwargs_and_weights(\n", + " # you can pass your own list of dataset names and sample weights here, but we've\n", + " # also provided a few named mixes for convenience. The ORCA model was trained\n", + " # using the \"oxe_magic_soup\" mix.\n", + " \"rtx\",\n", + " # can be local or on cloud storage (anything supported by TFDS)\n", + " # \"/path/to/base/oxe/directory\",\n", + " \"gs://rail-orca-central2/resize_256_256\",\n", + " # let's get a wrist camera!\n", + " load_camera_views=(\"primary\", \"wrist\"),\n", + ")\n", + "\n", + "# see `orca.data.dataset.make_dataset_from_rlds` for the meaning of these kwargs\n", + "dataset_kwargs_list[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-10 19:39:09.855670: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2256] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n", + "Skipping registering GPU devices...\n", + "2023-12-10 19:39:10.005777: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:39:12.102623: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:39:18.032340: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:39:19.651200: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:39:25.750807: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:39:27.660353: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] 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tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:39:51.630201: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:39:56.557120: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:39:57.931511: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:40:02.390246: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:40:03.489463: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:40:08.172129: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:40:09.519750: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:40:14.213931: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:40:15.516819: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "######################################################################################\n", + "# Loading the following 11 datasets (incl. sampling weight): #\n", + "# fractal20220817_data: ====================================================0.027916 #\n", + "# kuka: ====================================================================0.043051 #\n", + "# bridge_dataset: ==========================================================0.051613 #\n", + "# taco_play: ===============================================================0.103226 #\n", + "# jaco_play: ===============================================================0.103226 #\n", + "# berkeley_cable_routing: ==================================================0.154839 #\n", + "# roboturk: ================================================================0.051613 #\n", + "# nyu_door_opening_surprising_effectiveness: ===============================0.258065 #\n", + "# viola: ===================================================================0.103226 #\n", + "# berkeley_autolab_ur5: ====================================================0.051613 #\n", + "# toto: ====================================================================0.051613 #\n", + "######################################################################################\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-10 19:40:20.329889: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:40:25.751868: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:40:31.589180: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:40:36.783740: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:40:41.887236: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:40:46.315400: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:40:50.941568: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:40:56.087671: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:41:00.913807: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:41:05.028259: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "2023-12-10 19:41:10.518867: I tensorflow/core/grappler/optimizers/data/replicate_on_split.cc:32] Running replicate on split optimization\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "W0000 00:00:1702266073.641833 1937718 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: \"CropAndResize\" attr { key: \"T\" value { type: DT_FLOAT } } attr { key: \"extrapolation_value\" value { f: 0 } } attr { key: \"method\" value { s: \"bilinear\" } } inputs { dtype: DT_FLOAT shape { dim { size: 1 } dim { size: 256 } dim { size: 256 } dim { size: -26 } } } inputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -2 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: \"CPU\" vendor: \"GenuineIntel\" model: \"111\" frequency: 3298 num_cores: 12 environment { key: \"cpu_instruction_set\" value: \"AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2\" } environment { key: \"eigen\" value: \"3.4.90\" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 15728640 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -2 } dim { size: -30 } dim { size: -31 } dim { size: -26 } } }\n", + "W0000 00:00:1702266073.642149 1937718 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: \"CropAndResize\" attr { key: \"T\" value { type: DT_FLOAT } } attr { key: \"extrapolation_value\" value { f: 0 } } attr { key: \"method\" value { s: \"bilinear\" } } inputs { dtype: DT_FLOAT shape { dim { size: 1 } dim { size: 128 } dim { size: 128 } dim { size: -27 } } } inputs { dtype: DT_FLOAT shape { dim { size: -3 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -3 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: \"CPU\" vendor: \"GenuineIntel\" model: \"111\" frequency: 3298 num_cores: 12 environment { key: \"cpu_instruction_set\" value: \"AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2\" } environment { key: \"eigen\" value: \"3.4.90\" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 15728640 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -3 } dim { size: -32 } dim { size: -33 } dim { size: -27 } } }\n", + "W0000 00:00:1702266073.642285 1937718 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: \"CropAndResize\" attr { key: \"T\" value { type: DT_FLOAT } } attr { key: \"extrapolation_value\" value { f: 0 } } attr { key: \"method\" value { s: \"bilinear\" } } inputs { dtype: DT_FLOAT shape { dim { size: 1 } dim { size: 256 } dim { size: 256 } dim { size: -28 } } } inputs { dtype: DT_FLOAT shape { dim { size: -6 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -6 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: \"CPU\" vendor: \"GenuineIntel\" model: \"111\" frequency: 3298 num_cores: 12 environment { key: \"cpu_instruction_set\" value: \"AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2\" } environment { key: \"eigen\" value: \"3.4.90\" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 15728640 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -6 } dim { size: -40 } dim { size: -41 } dim { size: -28 } } }\n", + "W0000 00:00:1702266073.642336 1937718 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: \"CropAndResize\" attr { key: \"T\" value { type: DT_FLOAT } } attr { key: \"extrapolation_value\" value { f: 0 } } attr { key: \"method\" value { s: \"bilinear\" } } inputs { dtype: DT_FLOAT shape { dim { size: 1 } dim { size: 256 } dim { size: 256 } dim { size: -28 } } } inputs { dtype: DT_FLOAT shape { dim { size: -7 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -7 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: \"CPU\" vendor: \"GenuineIntel\" model: \"111\" frequency: 3298 num_cores: 12 environment { key: \"cpu_instruction_set\" value: \"AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2\" } environment { key: \"eigen\" value: \"3.4.90\" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 15728640 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -7 } dim { size: -42 } dim { size: -43 } dim { size: -28 } } }\n", + "W0000 00:00:1702266073.642380 1937718 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: \"CropAndResize\" attr { key: \"T\" value { type: DT_FLOAT } } attr { key: \"extrapolation_value\" value { f: 0 } } attr { key: \"method\" value { s: \"bilinear\" } } inputs { dtype: DT_FLOAT shape { dim { size: 1 } dim { size: 128 } dim { size: 128 } dim { size: -29 } } } inputs { dtype: DT_FLOAT shape { dim { size: -8 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -8 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: \"CPU\" vendor: \"GenuineIntel\" model: \"111\" frequency: 3298 num_cores: 12 environment { key: \"cpu_instruction_set\" value: \"AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2\" } environment { key: \"eigen\" value: \"3.4.90\" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 15728640 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -8 } dim { size: -44 } dim { size: -45 } dim { size: -29 } } }\n", + "W0000 00:00:1702266073.642482 1937718 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: \"CropAndResize\" attr { key: \"T\" value { type: DT_FLOAT } } attr { key: \"extrapolation_value\" value { f: 0 } } attr { key: \"method\" value { s: \"bilinear\" } } inputs { dtype: DT_FLOAT shape { dim { size: 1 } dim { size: 128 } dim { size: 128 } dim { size: -29 } } } inputs { dtype: DT_FLOAT shape { dim { size: -9 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -9 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } } device { type: \"CPU\" vendor: \"GenuineIntel\" model: \"111\" frequency: 3298 num_cores: 12 environment { key: \"cpu_instruction_set\" value: \"AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2\" } environment { key: \"eigen\" value: \"3.4.90\" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 15728640 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -9 } dim { size: -46 } dim { size: -47 } dim { size: -29 } } }\n" + ] + } + ], + "source": [ + "SHUFFLE_BUFFER_SIZE = 1000\n", + "BATCH_SIZE = 8\n", + "\n", + "# each element of `dataset_kwargs_list` can be used with `make_single_dataset`, but let's\n", + "# use the more powerful `make_interleaved_dataset` to combine them for us!\n", + "dataset = make_interleaved_dataset(\n", + " dataset_kwargs_list,\n", + " sample_weights,\n", + " train=True,\n", + " # unlike our manual shuffling above, `make_interleaved_dataset` will shuffle\n", + " # the JPEG-encoded images, so you should be able to fit a much larger buffer size\n", + " shuffle_buffer_size=SHUFFLE_BUFFER_SIZE,\n", + " batch_size=BATCH_SIZE,\n", + " # see `orca.data.dataset.apply_trajectory_transforms` for full documentation\n", + " # of these configuration options\n", + " traj_transform_kwargs=dict(\n", + " goal_relabeling_strategy=\"uniform\", # let's get some goal images\n", + " window_size=2, # let's get some history\n", + " future_action_window_size=3, # let's get some future actions for action chunking\n", + " subsample_length=100, # subsampling long trajectories improves shuffling a lot\n", + " # let's filter outlier actions just in case (this is after normalization, so\n", + " # 4.0 represents 4 standard deviations from the mean)\n", + " max_action=4.0,\n", + " ),\n", + " # see `orca.data.dataset.apply_frame_transforms` for full documentation\n", + " # of these configuration options\n", + " frame_transform_kwargs=dict(\n", + " # let's apply some basic image augmentations -- see `dlimp.transforms.augment_image`\n", + " # for full documentation of these configuration options\n", + " image_augment_kwargs=dict(\n", + " augment_order=[\"random_resized_crop\", \"random_brightness\"],\n", + " random_resized_crop=dict(scale=[0.8, 1.0], ratio=[0.9, 1.1]),\n", + " random_brightness=[0.1],\n", + " ),\n", + " # provided a `resize_size` is highly recommended for a mixed dataset, otherwise\n", + " # datasets with different resolutions will cause errors\n", + " resize_size=dict(\n", + " primary=(256, 256),\n", + " wrist=(128, 128),\n", + " ),\n", + " # If parallelism options are not provided, they will default to tf.Data.AUTOTUNE.\n", + " # However, we would highly recommend setting them manually if you run into issues\n", + " # with memory or dataloading speed. Frame transforms are usually the speed\n", + " # bottleneck (due to image decoding, augmentation, and resizing), so you can set\n", + " # this to a very high value if you have a lot of CPU cores. Keep in mind that more\n", + " # parallel calls also use more memory, though.\n", + " num_parallel_calls=64,\n", + " ),\n", + " # Same spiel as above about performance, although trajectory transforms and data reading\n", + " # are usually not the speed bottleneck. One reason to manually set these is if you want\n", + " # to reduce memory usage (since autotune may spawn way more threads than necessary).\n", + " traj_transform_threads=16,\n", + " traj_read_threads=16,\n", + ")\n", + "\n", + "# Another performance knob to tune is the number of batches to prefetch -- again,\n", + "# the default of tf.data.AUTOTUNE can sometimes use more memory than necessary.\n", + "iterator = dataset.iterator(prefetch=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top-level keys: dict_keys(['observation', 'task', 'action', 'dataset_name', 'absolute_action_mask'])\n", + "Observation keys: dict_keys(['image_primary', 'image_wrist', 'proprio', 'timestep', 'pad_mask_dict', 'pad_mask'])\n", + "Task keys: dict_keys(['language_instruction', 'pad_mask_dict', 'image_primary', 'image_wrist', 'proprio', 'timestep'])\n" + ] + } + ], + "source": [ + "# phew, that was a lot of configuration! Let's see what we got.\n", + "batch = next(iterator)\n", + "print(\"Top-level keys: \", batch.keys())\n", + "# should now have \"image_primary\" and \"image_wrist\"!\n", + "print(\"Observation keys: \", batch[\"observation\"].keys())\n", + "# should also have \"image_primary\" and \"image_wrist\", corresponding to future goal images\n", + "print(\"Task keys: \", batch[\"task\"].keys())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(8, 2, 256, 256, 3)\n", + "(8, 2, 128, 128, 3)\n", + "(8, 5, 7)\n" + ] + } + ], + "source": [ + "from PIL import Image\n", + "import numpy as np\n", + "\n", + "images_primary = batch[\"observation\"][\"image_primary\"]\n", + "images_wrist = batch[\"observation\"][\"image_wrist\"]\n", + "# should be: (batch_size, window_size (now 2), height, width, channels)\n", + "print(images_primary.shape)\n", + "print(images_wrist.shape)\n", + "actions = batch[\"action\"]\n", + "# should be: (batch_size, window_size + future_action_window_size (so 5), action_dim)\n", + "print(actions.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ True True]\n" + ] + } + ], + "source": [ + "# let's visualize a window of primary images\n", + "display(Image.fromarray(np.concatenate(images_primary[0], axis=1)))\n", + "# now a window of wrist images -- many datasets don't have wrist images,\n", + "# so this will often be black\n", + "display(Image.fromarray(np.concatenate(images_wrist[0], axis=1)))\n", + "# pad_mask_dict also tells you which keys should be treated as padding\n", + "# (e.g., if the wrist camera is black, the corresponding pad_mask_dict entry is False)\n", + "print(batch[\"observation\"][\"pad_mask_dict\"][\"image_wrist\"][0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "b'push left the yellow block'\n" + ] + } + ], + "source": [ + "# let's take a look at the \"task\" dict: it should now have both goal\n", + "# images and language instructions!\n", + "goal_primary = batch[\"task\"][\"image_primary\"]\n", + "goal_wrist = batch[\"task\"][\"image_wrist\"]\n", + "language_instruction = batch[\"task\"][\"language_instruction\"]\n", + "display(Image.fromarray(goal_primary[0]))\n", + "display(Image.fromarray(goal_wrist[0]))\n", + "print(language_instruction[0])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "orca-2", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 5f0d7e7c981263a903f018e53c9acaa9d1e8f54b Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Sun, 10 Dec 2023 22:35:47 -0800 Subject: [PATCH 24/25] Get rid of common_dataset_kwargs --- config.py | 5 ----- tests/debug_config.py | 1 + train.py | 8 -------- 3 files changed, 1 insertion(+), 13 deletions(-) diff --git a/config.py b/config.py index b3d051b0..64f0b865 100644 --- a/config.py +++ b/config.py @@ -126,11 +126,6 @@ def get_dataset_config(modality="multimodal", window_size=1): load_camera_views=("primary", "wrist"), load_depth=False, ), - # common_dataset_kwargs override specific kwargs from dataset_kwargs_list - "common_dataset_kwargs": dict( - action_proprio_normalization_type=normalization_type, - language_key="language_instruction", - ), "traj_transform_kwargs": dict( window_size=window_size, future_action_window_size=0, diff --git a/tests/debug_config.py b/tests/debug_config.py index b9904ae0..371c949c 100644 --- a/tests/debug_config.py +++ b/tests/debug_config.py @@ -36,6 +36,7 @@ def get_config(): "data_dir": "./tests/debug_dataset", "image_obs_keys": {"primary": "image_0"}, "state_obs_keys": ["state"], + "language_key": "language_instruction", }, ], "frame_transform_kwargs": { diff --git a/train.py b/train.py index 22b4d130..27002608 100644 --- a/train.py +++ b/train.py @@ -159,14 +159,6 @@ def process_batch(batch): ) del FLAGS.config.dataset_kwargs["oxe_kwargs"] - # override each element of dataset_kwargs_list with common_dataset_kwargs - if "common_dataset_kwargs" in FLAGS.config.dataset_kwargs: - FLAGS.config.dataset_kwargs["dataset_kwargs_list"] = [ - {**kwargs, **FLAGS.config.dataset_kwargs["common_dataset_kwargs"]} - for kwargs in FLAGS.config.dataset_kwargs["dataset_kwargs_list"] - ] - del FLAGS.config.dataset_kwargs["common_dataset_kwargs"] - train_data = make_interleaved_dataset(**FLAGS.config.dataset_kwargs, train=True) # save dataset statistics From e0c1d5f9783dd8086dde065c702bd0123b52aa80 Mon Sep 17 00:00:00 2001 From: Kevin Black Date: Sun, 10 Dec 2023 22:44:13 -0800 Subject: [PATCH 25/25] Fix unused imports --- examples/envs/aloha_sim_env.py | 4 ---- examples/eval_finetuned.py | 1 - examples/eval_finetuned_on_robot.py | 9 ++------- examples/finetune_new_observation_action.py | 3 +-- 4 files changed, 3 insertions(+), 14 deletions(-) diff --git a/examples/envs/aloha_sim_env.py b/examples/envs/aloha_sim_env.py index 652173c9..acd73684 100644 --- a/examples/envs/aloha_sim_env.py +++ b/examples/envs/aloha_sim_env.py @@ -1,12 +1,8 @@ import copy -import os from typing import List -import cv2 import dlimp as dl -from einops import rearrange import gym -import jax import jax.numpy as jnp import numpy as np diff --git a/examples/eval_finetuned.py b/examples/eval_finetuned.py index 83c83e1d..29baa700 100644 --- a/examples/eval_finetuned.py +++ b/examples/eval_finetuned.py @@ -19,7 +19,6 @@ import wandb sys.path.append("/nfs/nfs2/users/karl/code/act") -from envs.aloha_sim_env import AlohaGymEnv from orca.utils.gym_wrappers import HistoryWrapper, RHCWrapper, UnnormalizeActionProprio from orca.utils.pretrained_utils import ORCAModel diff --git a/examples/eval_finetuned_on_robot.py b/examples/eval_finetuned_on_robot.py index 0f5eefa9..76f16c8d 100644 --- a/examples/eval_finetuned_on_robot.py +++ b/examples/eval_finetuned_on_robot.py @@ -23,16 +23,11 @@ from orca.utils.eval_utils import ( download_checkpoint_from_gcs, - load_jaxrlm_checkpoint, sample_actions, supply_rng, ) -from orca.utils.gym_wrappers import ( - HistoryWrapper, - RHCWrapper, - TemporalEnsembleWrapper, - UnnormalizeActionProprio, -) +from orca.utils.gym_wrappers import HistoryWrapper, RHCWrapper, UnnormalizeActionProprio +from orca.utils.gym_wrappers import TemporalEnsembleWrapper # noqa: F401 from orca.utils.pretrained_utils import ORCAModel np.set_printoptions(suppress=True) diff --git a/examples/finetune_new_observation_action.py b/examples/finetune_new_observation_action.py index 6880aa06..56bb81f6 100644 --- a/examples/finetune_new_observation_action.py +++ b/examples/finetune_new_observation_action.py @@ -13,8 +13,7 @@ import wandb from orca.data.dataset import make_single_dataset -from orca.data.utils.data_utils import ActionEncoding, StateEncoding -from orca.data.utils.text_processing import text_processors +from orca.data.oxe.oxe_dataset_configs import ActionEncoding, StateEncoding from orca.utils.jax_utils import initialize_compilation_cache from orca.utils.pretrained_utils import ORCAModel from orca.utils.train_callbacks import SaveCallback