pi zero pytorch下載 - pi zero pytorch源碼下載

pi zero pytorch

Ai源碼

0.1.5

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pi-zero-pytorch (在製品)

物理智慧提出的機器人基礎模型架構π₀的實現

這項工作的總結是，它是一個簡化的 Transfusion（Zhou 等人），受到 Stable Diffusion 3（Esser 等人）的影響，主要是採用流匹配而不是擴散來生成策略，以及分離參數（來自mmDIT 的聯合注意力）。它們建立在預先訓練的視覺語言模型 PaliGemma 2B 之上。

欣賞

Einops 用於令人驚嘆的打包和解包，在這裡廣泛用於管理各種令牌集
Flex Attention 可輕鬆混合自回歸和雙向注意力
@Wonder1905 用於程式碼審查和識別問題
你？也許是博士生想要為行為複製的最新 SOTA 架構做出貢獻？

安裝

$ pip install pi-zero-pytorch

用法

 import torch
from pi_zero_pytorch import π0

model = π0 (
    dim = 512 ,
    dim_action_input = 6 ,
    dim_joint_state = 12 ,
    num_tokens = 20_000
)

vision = torch . randn ( 1 , 1024 , 512 )
commands = torch . randint ( 0 , 20_000 , ( 1 , 1024 ))
joint_state = torch . randn ( 1 , 12 )
actions = torch . randn ( 1 , 32 , 6 )

loss , _ = model ( vision , commands , joint_state , actions )
loss . backward ()

# after much training

sampled_actions = model ( vision , commands , joint_state , trajectory_length = 32 ) # (1, 32, 6)

貢獻

在專案根目錄下，執行

$ pip install ' .[test] ' # or `uv pip install '.[test]'`

然後將測試新增至tests/test_pi_zero.py並執行

$ pytest tests/

就是這樣

引文

 @misc { Black2024 ,
    author  = { Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Lucy Xiaoyang Shi, James Tanner, Quan Vuong, Anna Walling, Haohuan Wang, Ury Zhilinsky } ,
    url     = { https://www.physicalintelligence.company/download/pi0.pdf }
}

 @inproceedings { Zhou2024ValueRL ,
    title   = { Value Residual Learning For Alleviating Attention Concentration In Transformers } ,
    author  = { Zhanchao Zhou and Tianyi Wu and Zhiyun Jiang and Zhenzhong Lan } ,
    year    = { 2024 } ,
    url     = { https://api.semanticscholar.org/CorpusID:273532030 }
}

 @inproceedings { Darcet2023VisionTN ,
    title   = { Vision Transformers Need Registers } ,
    author  = { Timoth'ee Darcet and Maxime Oquab and Julien Mairal and Piotr Bojanowski } ,
    year    = { 2023 } ,
    url     = { https://api.semanticscholar.org/CorpusID:263134283 }
}

 @article { Li2024ImmiscibleDA ,
    title   = { Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment } ,
    author  = { Yiheng Li and Heyang Jiang and Akio Kodaira and Masayoshi Tomizuka and Kurt Keutzer and Chenfeng Xu } ,
    journal = { ArXiv } ,
    year    = { 2024 } ,
    volume  = { abs/2406.12303 } ,
    url     = { https://api.semanticscholar.org/CorpusID:270562607 }
}

 @inproceedings { Sadat2024EliminatingOA ,
    title   = { Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion Models } ,
    author  = { Seyedmorteza Sadat and Otmar Hilliges and Romann M. Weber } ,
    year    = { 2024 } ,
    url     = { https://api.semanticscholar.org/CorpusID:273098845 }
}

 @article { Bulatov2022RecurrentMT ,
    title   = { Recurrent Memory Transformer } ,
    author  = { Aydar Bulatov and Yuri Kuratov and Mikhail S. Burtsev } ,
    journal = { ArXiv } ,
    year    = { 2022 } ,
    volume  = { abs/2207.06881 } ,
    url     = { https://api.semanticscholar.org/CorpusID:250526424 }
}

 @inproceedings { Bessonov2023RecurrentAT ,
    title   = { Recurrent Action Transformer with Memory } ,
    author  = { A. B. Bessonov and Alexey Staroverov and Huzhenyu Zhang and Alexey K. Kovalev and D. Yudin and Aleksandr I. Panov } ,
    year    = { 2023 } ,
    url     = { https://api.semanticscholar.org/CorpusID:259188030 }
}

 @article { Zhu2024HyperConnections ,
    title   = { Hyper-Connections } ,
    author  = { Defa Zhu and Hongzhi Huang and Zihao Huang and Yutao Zeng and Yunyao Mao and Banggu Wu and Qiyang Min and Xun Zhou } ,
    journal = { ArXiv } ,
    year    = { 2024 } ,
    volume  = { abs/2409.19606 } ,
    url     = { https://api.semanticscholar.org/CorpusID:272987528 }
}