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b6a9d87 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from abc import ABC, abstractmethod
from typing import Any
import numpy as np
from transformers import ProcessorMixin
from gr00t.data.types import EmbodimentTag, ModalityConfig
class BaseProcessor(ProcessorMixin):
def __call__(self, messages: list[dict[str, Any]]) -> dict[str, Any]:
"""
Process a list of messages and return a dictionary of model inputs.
Args:
messages (list[dict[str, Any]]): List of messages to process.
Returns:
dict[str, Any]: Dictionary of model inputs.
Example:
>>> processor = BaseProcessor()
>>> messages = [
>>> {"type": MessageType.START_OF_EPISODE.value, "content": ""},
>>> {"type": MessageType.EPISODE_STEP.value, "content": VLAStepData},
>>> {"type": MessageType.TEXT.value, "role" : "user", "content": "Please give me the apple"},
>>> {"type": MessageType.TEXT.value, "role" : "assistant", "content": "I need to move my left hand to get the apple"},
>>> {"type": MessageType.EPISODE_STEP.value, "content": VLAStepData},
>>> {"type": MessageType.EPISODE_STEP.value, "content": VLAStepData},
>>> {"type": MessageType.END_OF_EPISODE.value, "content": ""},
>>> ]
>>> model_input = processor(messages)
>>> print(model_input)
"""
raise NotImplementedError("Subclasses must implement __call__")
def decode_action(
self,
action: np.ndarray,
embodiment_tag: EmbodimentTag,
state: dict[str, np.ndarray] | None = None,
) -> dict[str, np.ndarray]:
"""Decode the action from the model output."""
raise NotImplementedError("Subclasses must implement decode_action")
@property
def collator(self):
raise NotImplementedError("Subclasses must implement collator")
@abstractmethod
def set_statistics(self, statistics: dict[str, Any], override: bool = False) -> None:
"""Set normalization statistics."""
pass
def train(self):
self.training = True
def eval(self):
self.training = False
def get_modality_configs(self) -> dict[str, dict[str, ModalityConfig]]:
"""Get the modality configurations.
Returns:
dict[str, dict[str, ModalityConfig]]: The modality configurations, where
modality_configs[embodiment_tag][modality] = ModalityConfig
"""
return getattr(self, "modality_configs")
class ShardedDataset(ABC):
def __init__(self, dataset_path):
self.dataset_path = dataset_path
@abstractmethod
def __len__(self) -> int:
"""Return the number of shards."""
pass
@abstractmethod
def get_shard_length(self, idx: int) -> int:
"""Get the length of the shard at index idx."""
pass
@abstractmethod
def get_shard(self, idx: int) -> list:
"""Get the shard at index idx."""
pass
def set_processor(self, processor: BaseProcessor):
self.processor = processor
def get_dataset_statistics(self) -> dict[str, Any]:
"""Get the dataset statistics. This is only required for dataloaders for robtics datasets."""
raise NotImplementedError()
# # Example chat formats (processor input)
# # Single step
# messages = [
# {"type": "episode_step", "content": VLAStepData},
# ]
# # Single episode
# messages = [
# {"type": MessageType.START_OF_EPISODE.value, "content": ""},
# {"type": MessageType.EPISODE_STEP.value, "content": VLAStepData},
# {"type": MessageType.EPISODE_STEP.value, "content": VLAStepData},
# {"type": MessageType.EPISODE_STEP.value, "content": VLAStepData},
# {"type": MessageType.END_OF_EPISODE.value, "content": ""},
# ]
# # Multiple episodes
# messages = [
# {"type": MessageType.START_OF_EPISODE.value, "content": ""},
# {"type": MessageType.EPISODE_STEP.value, "content": VLAStepData},
# {"type": MessageType.EPISODE_STEP.value, "content": VLAStepData},
# {"type": MessageType.END_OF_EPISODE.value, "content": ""},
# {"type": MessageType.START_OF_EPISODE.value, "content": ""},
# {"type": MessageType.EPISODE_STEP.value, "content": VLAStepData},
# {"type": MessageType.EPISODE_STEP.value, "content": VLAStepData},
# {"type": MessageType.END_OF_EPISODE.value, "content": ""},
# ]
# # Example usage
# messages = dataset[idx]
# model_input = processor(messages)
# model_output = model(**model_input) # or model.generate(**model_input)
# decoded_action = processor.decode_action(model_output)
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