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vllm_omni.diffusion.models.gr00t.modeling.processing_gr00t_n1d7

EMBODIMENT_TAG_TO_PROJECTOR_INDEX module-attribute 

EMBODIMENT_TAG_TO_PROJECTOR_INDEX = {
    "oxe_droid_relative_eef_relative_joint": 24,
    "xdof_relative_eef_relative_joint": 27,
    "xdof_relative_eef_relative_joint_subtask": 27,
    "real_g1_relative_eef_relative_joints": 25,
    "real_r1_pro_sharpa_relative_eef": 26,
    "real_r1_pro_sharpa_relative_eef_human": 26,
    "real_r1_pro_sharpa_relative_eef_maxinsights": 26,
    "real_r1_pro_sharpa_relative_eef_mecka": 26,
    "unitree_g1_full_body_with_waist_height_nav_cmd": 25,
    "unitree_g1_sonic": 11,
    "simpler_env_google": 0,
    "simpler_env_widowx": 1,
    "libero_sim": 2,
    "new_embodiment": 10,
}

QWEN3_VL_2B_PROCESSOR module-attribute 

QWEN3_VL_2B_PROCESSOR = 'Qwen/Qwen3-VL-2B-Instruct'

logger module-attribute 

logger = init_logger(__name__)

Gr00tN1d7DataCollator

model_name instance-attribute 

model_name = model_name

model_type instance-attribute 

model_type = model_type

processor instance-attribute 

processor = build_processor(
    model_name, transformers_loading_kwargs
)

Gr00tN1d7Processor

Bases: ProcessorMixin

apply_sincos_state_encoding instance-attribute 

apply_sincos_state_encoding = apply_sincos_state_encoding

clip_outliers instance-attribute 

clip_outliers = clip_outliers

collator property 

collator

color_jitter_params instance-attribute 

color_jitter_params = color_jitter_params

crop_fraction instance-attribute 

crop_fraction = crop_fraction

data_collator_class class-attribute instance-attribute 

data_collator_class = Gr00tN1d7DataCollator

embodiment_id_mapping instance-attribute 

embodiment_id_mapping = (
    embodiment_id_mapping
    or EMBODIMENT_TAG_TO_PROJECTOR_INDEX
)

eval_image_transform instance-attribute 

eval_image_transform = _build_eval_image_transform(
    image_target_size, image_crop_size
)

exclude_state instance-attribute 

exclude_state = exclude_state

formalize_language instance-attribute 

formalize_language = formalize_language

image_crop_size instance-attribute 

image_crop_size = image_crop_size

image_target_size instance-attribute 

image_target_size = image_target_size

max_action_dim instance-attribute 

max_action_dim = max_action_dim

max_action_horizon instance-attribute 

max_action_horizon = max_action_horizon

max_state_dim instance-attribute 

max_state_dim = max_state_dim

modality_configs instance-attribute 

modality_configs = parse_modality_configs(modality_configs)

model_name instance-attribute 

model_name = model_name

model_type instance-attribute 

model_type = model_type

processor instance-attribute 

processor = build_processor(
    model_name, transformers_loading_kwargs
)

random_rotation_angle instance-attribute 

random_rotation_angle = random_rotation_angle

shortest_image_edge instance-attribute 

shortest_image_edge = shortest_image_edge

state_action_processor instance-attribute 

state_action_processor = StateActionProcessor(
    modality_configs=modality_configs,
    statistics=statistics,
    use_percentiles=use_percentiles,
    clip_outliers=clip_outliers,
    apply_sincos_state_encoding=apply_sincos_state_encoding,
    use_relative_action=use_relative_action,
)

statistics instance-attribute 

statistics: dict[
    str, dict[str, dict[str, dict[str, list[float]]]]
] = {}

use_mean_std instance-attribute 

use_mean_std = use_mean_std

use_percentiles instance-attribute 

use_percentiles = use_percentiles

use_relative_action instance-attribute 

use_relative_action = use_relative_action

decode_action 

decode_action(
    action: ndarray,
    embodiment_tag: EmbodimentTag,
    state: dict[str, ndarray] | None = None,
)

Undo action normalization and convert relative actions to absolute.

from_pretrained classmethod 

from_pretrained(
    pretrained_model_name_or_path: str | Path, **kwargs
)

save_pretrained 

save_pretrained(save_directory: str | Path) -> list[Path]

set_statistics 

set_statistics(
    statistics: dict[
        str, dict[str, dict[str, dict[str, list[float]]]]
    ],
    override: bool = False,
) -> None

Set dataset statistics for normalization.

LetterBoxTransform

Pad image to square dimensions by adding black bars to the smaller side.

build_processor 

build_processor(
    model_name: str, transformers_loading_kwargs: dict
) -> ProcessorMixin