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github.com/SJTU-DENG-Lab/WLA
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Functions
234 in github.com/SJTU-DENG-Lab/WLA
⨍
Functions
234
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Types & classes
41
↓ 1 callers
Method
from_usr_args
(cls, usr_args: dict[str, Any])
experiments/robotwin/robotwin_utils.py:180
↓ 1 callers
Method
from_usr_args
(cls, usr_args: dict[str, Any])
experiments/rmbench/rmbench_utils.py:245
↓ 1 callers
Function
generate_seen_tasks_json
()
utils_dataset/get_robotwin_seen_instruction.py:5
↓ 1 callers
Method
get_eval_dataloader
(self, eval_dataset: Optional[Dataset] = None)
trainer.py:123
↓ 1 callers
Function
get_full_dirs
(training_args)
trainer_utils.py:30
↓ 1 callers
Method
get_input_embeddings
(self)
models/wla.py:104
↓ 1 callers
Function
get_last_line_as_float
(file_path)
utils/calc_robotwin_results.py:3
↓ 1 callers
Function
get_libero_dummy_action
Get dummy/no-op action, used to roll out the simulation while the robot does nothing.
experiments/libero/libero_utils.py:50
↓ 1 callers
Function
get_libero_env
Initializes and returns the LIBERO environment, along with the task description.
experiments/libero/libero_utils.py:40
↓ 1 callers
Function
get_libero_image
(history_obs, obs, resize_size)
experiments/libero/libero_utils.py:55
↓ 1 callers
Function
get_robocoin_list
(base_dir, target_robots)
utils/data_utils.py:42
↓ 1 callers
Function
get_save_image
(obs)
experiments/libero/libero_utils.py:81
↓ 1 callers
Method
get_sigmas
(self, timesteps, device, n_dim=4, dtype=torch.float32)
models/wla.py:116
↓ 1 callers
Function
get_train_datasets
(data_args, training_args, model_args, tokenize_func, tokenizer)
dataset.py:843
↓ 1 callers
Function
index_episodes
(root_dir)
utils/data_utils.py:28
↓ 1 callers
Function
index_episodes_egodex
(dataset_path)
utils/data_utils.py:7
↓ 1 callers
Method
inference
( self, observation: dict, instruction: str, )
experiments/libero/wla_utils.py:63
↓ 1 callers
Function
load_libero_dataset
(data_args, model_args, training_args)
dataset.py:1222
↓ 1 callers
Function
load_piper_dataset
(data_args, model_args, training_args)
dataset.py:1358
↓ 1 callers
Function
load_robocoin_dataset
(robot_dataset, data_args, model_args, training_args)
dataset.py:1392
↓ 1 callers
Function
main
()
utils_dataset/check_robotwin_videos.py:50
↓ 1 callers
Function
main
()
utils_dataset/generate_robotwin_instruction_parquet.py:18
↓ 1 callers
Function
main
()
utils/calc_robotwin_results.py:15
↓ 1 callers
Function
main
()
experiments/robotwin/run_robotwin_eval.py:179
↓ 1 callers
Function
main
()
experiments/rmbench/run_rmbench_eval.py:213
↓ 1 callers
Method
on_save
(self, args, state, control, model, **kwargs)
trainer.py:409
↓ 1 callers
Function
parse_args
()
experiments/robotwin/run_robotwin_eval.py:150
↓ 1 callers
Function
parse_args
()
experiments/rmbench/run_rmbench_eval.py:178
↓ 1 callers
Method
predict_action
(self, vl_embs_list: list, state: torch.Tensor = None)
models/action_model/action_model.py:259
↓ 1 callers
Function
quat2axisangle
Copied from robosuite: https://github.com/ARISE-Initiative/robosuite/blob/eafb81f54ffc104f905ee48a16bb15f059176ad3/robosuite/utils/transform_util
experiments/libero/libero_utils.py:103
↓ 1 callers
Method
reset
(self)
experiments/robotwin/robotwin_utils.py:219
↓ 1 callers
Function
resize_with_pad
(img, size)
experiments/libero/libero_utils.py:22
↓ 1 callers
Function
resolve_existing_file
(path_value: str, wla_root: Path, rmbench_root: Path)
experiments/rmbench/run_rmbench_eval.py:110
↓ 1 callers
Method
sample_images
( self, caption="", input_images=None, guidance_scale: float = 3.0, im
models/wla.py:413
↓ 1 callers
Method
sample_time
(self, batch_size, device, dtype)
models/action_model/action_model.py:149
↓ 1 callers
Function
save_rollout_video
Saves an MP4 replay of an episode.
experiments/libero/libero_utils.py:87
↓ 1 callers
Function
set_seed_everywhere
Sets the random seed for Python, NumPy, and PyTorch functions.
experiments/libero/run_libero_eval.py:57
↓ 1 callers
Function
stage_policy_adapter
(args, wla_root: Path, robotwin_root: Path)
experiments/robotwin/run_robotwin_eval.py:46
↓ 1 callers
Function
stage_policy_adapter
(args, wla_root: Path, rmbench_root: Path)
experiments/rmbench/run_rmbench_eval.py:66
↓ 1 callers
Function
str_to_bool
(value)
experiments/robotwin/run_robotwin_eval.py:13
↓ 1 callers
Function
str_to_bool
(value)
experiments/rmbench/run_rmbench_eval.py:29
↓ 1 callers
Function
unnormalize_and_unpad
(data, norm_stats_key, original_dim, add_eps=True)
utils/transforms.py:59
↓ 1 callers
Function
validate_norm_stats
(args, wla_root: Path, rmbench_root: Path)
experiments/rmbench/run_rmbench_eval.py:126
↓ 1 callers
Function
validate_rmbench_root
(rmbench_root: Path)
experiments/rmbench/run_rmbench_eval.py:101
↓ 1 callers
Function
validate_robotwin_root
(robotwin_root: Path)
experiments/robotwin/run_robotwin_eval.py:94
Method
__call__
(self, tensor)
trainer_utils.py:13
Method
__getitem__
(self, idx)
dataset.py:246
Method
__getitem__
(self, idx)
dataset.py:404
Method
__getitem__
(self, idx)
dataset.py:530
Method
__getitem__
(self, idx)
dataset.py:556
Method
__getitem__
(self, idx)
dataset.py:605
Method
__getitem__
(self, idx)
dataset.py:658
Method
__getitem__
(self, idx)
dataset.py:710
Method
__init__
(self, processor)
trainer_utils.py:10
Method
__init__
(self, base_dataset, image_key, max_size=None)
dataset.py:65
Method
__init__
( self, base_dataset, target_transform, primary_image_size, auxiliary
dataset.py:205
Method
__init__
( self, base_dataset, primary_image_size, auxiliary_image_size, prim
dataset.py:365
Method
__init__
(self, primary_dataset, secondary_dataset)
dataset.py:518
Method
__init__
(self, dataset_path, model_args)
dataset.py:538
Method
__init__
( self, raw_dataset, target_transform, primary_image_size, auxiliar
dataset.py:586
Method
__init__
(self, dataset_path, model_args)
dataset.py:639
Method
__init__
( self, raw_dataset, target_transform, primary_image_size, auxiliar
dataset.py:689
Method
__init__
( self, model_id: str, checkpoints_dir: str, norm_file_path: str, unno
experiments/libero/wla_utils.py:17
Method
__init__
( self, *, wla_root: Path, model_id: str, checkpoints_dir: Optional[st
experiments/robotwin/robotwin_utils.py:116
Method
__init__
( self, *, wla_root: Path, model_id: str, checkpoints_dir: Optional[st
experiments/rmbench/rmbench_utils.py:159
Method
__init__
( self, vae_id: str = "SJTU-DENG-Lab/Sana_600M_512px_diffusers_64channels", input_size
models/wla.py:27
Method
__init__
(self, dim, heads=1)
models/transformer_encoder.py:21
Method
__init__
(self, config: Qwen2Config, layer_idx: Optional[int] = None)
models/transformer_encoder.py:36
Method
__init__
(self, config: Qwen2Config, layer_idx: int)
models/transformer_encoder.py:89
Method
__init__
( self, mllm_id: str = "Alibaba-DAMO-Academy/RynnBrain-2B", diffusion_model_id: str =
models/model.py:21
Method
__init__
(self, embedding_dim)
models/action_model/action_encoder.py:30
Method
__init__
(self, num_categories, input_dim, hidden_dim)
models/action_model/action_model.py:26
Method
__init__
(self, num_categories, input_dim, hidden_dim, output_dim)
models/action_model/action_model.py:41
Method
__init__
(self, action_dim, hidden_size)
models/action_model/action_model.py:64
Method
__init__
( self, config, **kwargs, )
models/action_model/action_model.py:112
Method
__init__
(self, embedding_dim, compute_dtype=torch.float32)
models/action_model/cross_attention_dit.py:32
Method
__init__
( self, embedding_dim: int, norm_elementwise_affine: bool = False, norm_eps: f
models/action_model/cross_attention_dit.py:45
Method
__init__
( self, dim: int, num_attention_heads: int, attention_head_dim: int, d
models/action_model/cross_attention_dit.py:71
Method
__init__
( self, num_attention_heads: int = 8, attention_head_dim: int = 64, output_dim
models/action_model/cross_attention_dit.py:317
Method
__len__
(self)
dataset.py:243
Method
__len__
(self)
dataset.py:401
Method
__len__
(self)
dataset.py:527
Method
__len__
(self)
dataset.py:547
Method
__len__
(self)
dataset.py:602
Method
__len__
(self)
dataset.py:649
Method
__len__
(self)
dataset.py:707
Method
__post_init__
(self)
train.py:168
Function
_collate_fn
(batch, tokenize_func, tokenizer, model_args)
dataset.py:746
Method
_init_weights
(self, module)
models/transformer_encoder.py:143
Method
_maybe_log_save_evaluate
( self, tr_loss, grad_norm, model, trial, epoch, ignor
trainer.py:273
Method
as_list
(value, default="")
models/model.py:225
Function
check_single_video
(video_path: str)
utils_dataset/check_robotwin_videos.py:22
Method
compute_loss
(self, model, inputs, return_outputs=False, num_items_in_batch=None)
trainer.py:239
Method
dtype
(self)
models/action_model/action_model.py:316
Function
eval
(TASK_ENV, model, observation)
experiments/robotwin/deploy_policy.py:20
Function
eval
(TASK_ENV, model, observation, history_observation=None)
experiments/rmbench/deploy_policy.py:20
Method
evaluate
( self, eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None, ignore_keys
trainer.py:43
Method
forward
( self, pixel_values=None, input_ids=None, attention_mask=None, **kwargs )
models/wla.py:133
Method
forward
(self, x)
models/transformer_encoder.py:26
Method
forward
( self, hidden_states: torch.Tensor, position_embeddings: Optional[Tuple[torch.Tensor,
models/transformer_encoder.py:50
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