Merge pull request #41 from alexander-soare/fix_pusht_diffusion
Fixes issues with PushT diffusion
This commit is contained in:
Remi
GitHub
commit
b633748987
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# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
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# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
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[[package]]
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name = "absl-py"
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@ -44,56 +44,56 @@ files = [
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{file = "av-12.0.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:746ab0eff8a7a21a6c6d16e6b6e61709527eba2ad1a524d92a01bb60d02a3df7"},
|
||||
{file = "av-12.0.0-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:013b3ac3de3aa1c137af0cedafd364fd1c7524ab3e1cd53e04564fd1632ac04d"},
|
||||
{file = "av-12.0.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0fa55923527648f51ac005e44fe2797ebc67f53ad4850e0194d3753761ee33a2"},
|
||||
{file = "av-12.0.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:35d514f4dee0cf67e9e6b2a65fb4a28f98da88e71e8c7f7960bd04625d9fe965"},
|
||||
{file = "av-12.0.0.tar.gz", hash = "sha256:bcf21ebb722d4538b4099e5a78f730d78814dd70003511c185941dba5651b14d"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -614,6 +614,16 @@ files = [
|
|||
[package.dependencies]
|
||||
six = ">=1.4.0"
|
||||
|
||||
[[package]]
|
||||
name = "egl-probe"
|
||||
version = "1.0.2"
|
||||
description = ""
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "egl_probe-1.0.2.tar.gz", hash = "sha256:29bdca7b08da1e060cfb42cd46af8300a7ac4f3b1b2eeb16e545ea16d9a5ac93"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "einops"
|
||||
version = "0.7.0"
|
||||
|
|
@ -705,13 +715,13 @@ typing = ["typing-extensions (>=4.8)"]
|
|||
|
||||
[[package]]
|
||||
name = "fsspec"
|
||||
version = "2024.2.0"
|
||||
version = "2024.3.1"
|
||||
description = "File-system specification"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "fsspec-2024.2.0-py3-none-any.whl", hash = "sha256:817f969556fa5916bc682e02ca2045f96ff7f586d45110fcb76022063ad2c7d8"},
|
||||
{file = "fsspec-2024.2.0.tar.gz", hash = "sha256:b6ad1a679f760dda52b1168c859d01b7b80648ea6f7f7c7f5a8a91dc3f3ecb84"},
|
||||
{file = "fsspec-2024.3.1-py3-none-any.whl", hash = "sha256:918d18d41bf73f0e2b261824baeb1b124bcf771767e3a26425cd7dec3332f512"},
|
||||
{file = "fsspec-2024.3.1.tar.gz", hash = "sha256:f39780e282d7d117ffb42bb96992f8a90795e4d0fb0f661a70ca39fe9c43ded9"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -810,6 +820,72 @@ files = [
|
|||
[package.extras]
|
||||
preview = ["glfw-preview"]
|
||||
|
||||
[[package]]
|
||||
name = "grpcio"
|
||||
version = "1.62.1"
|
||||
description = "HTTP/2-based RPC framework"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "grpcio-1.62.1-cp310-cp310-linux_armv7l.whl", hash = "sha256:179bee6f5ed7b5f618844f760b6acf7e910988de77a4f75b95bbfaa8106f3c1e"},
|
||||
{file = "grpcio-1.62.1-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:48611e4fa010e823ba2de8fd3f77c1322dd60cb0d180dc6630a7e157b205f7ea"},
|
||||
{file = "grpcio-1.62.1-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:b2a0e71b0a2158aa4bce48be9f8f9eb45cbd17c78c7443616d00abbe2a509f6d"},
|
||||
{file = "grpcio-1.62.1-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:fbe80577c7880911d3ad65e5ecc997416c98f354efeba2f8d0f9112a67ed65a5"},
|
||||
{file = "grpcio-1.62.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:58f6c693d446964e3292425e1d16e21a97a48ba9172f2d0df9d7b640acb99243"},
|
||||
{file = "grpcio-1.62.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:77c339403db5a20ef4fed02e4d1a9a3d9866bf9c0afc77a42234677313ea22f3"},
|
||||
{file = "grpcio-1.62.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:b5a4ea906db7dec694098435d84bf2854fe158eb3cd51e1107e571246d4d1d70"},
|
||||
{file = "grpcio-1.62.1-cp310-cp310-win32.whl", hash = "sha256:4187201a53f8561c015bc745b81a1b2d278967b8de35f3399b84b0695e281d5f"},
|
||||
{file = "grpcio-1.62.1-cp310-cp310-win_amd64.whl", hash = "sha256:844d1f3fb11bd1ed362d3fdc495d0770cfab75761836193af166fee113421d66"},
|
||||
{file = "grpcio-1.62.1-cp311-cp311-linux_armv7l.whl", hash = "sha256:833379943d1728a005e44103f17ecd73d058d37d95783eb8f0b28ddc1f54d7b2"},
|
||||
{file = "grpcio-1.62.1-cp311-cp311-macosx_10_10_universal2.whl", hash = "sha256:c7fcc6a32e7b7b58f5a7d27530669337a5d587d4066060bcb9dee7a8c833dfb7"},
|
||||
{file = "grpcio-1.62.1-cp311-cp311-manylinux_2_17_aarch64.whl", hash = "sha256:fa7d28eb4d50b7cbe75bb8b45ed0da9a1dc5b219a0af59449676a29c2eed9698"},
|
||||
{file = "grpcio-1.62.1-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:48f7135c3de2f298b833be8b4ae20cafe37091634e91f61f5a7eb3d61ec6f660"},
|
||||
{file = "grpcio-1.62.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:71f11fd63365ade276c9d4a7b7df5c136f9030e3457107e1791b3737a9b9ed6a"},
|
||||
{file = "grpcio-1.62.1-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:4b49fd8fe9f9ac23b78437da94c54aa7e9996fbb220bac024a67469ce5d0825f"},
|
||||
{file = "grpcio-1.62.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:482ae2ae78679ba9ed5752099b32e5fe580443b4f798e1b71df412abf43375db"},
|
||||
{file = "grpcio-1.62.1-cp311-cp311-win32.whl", hash = "sha256:1faa02530b6c7426404372515fe5ddf66e199c2ee613f88f025c6f3bd816450c"},
|
||||
{file = "grpcio-1.62.1-cp311-cp311-win_amd64.whl", hash = "sha256:5bd90b8c395f39bc82a5fb32a0173e220e3f401ff697840f4003e15b96d1befc"},
|
||||
{file = "grpcio-1.62.1-cp312-cp312-linux_armv7l.whl", hash = "sha256:b134d5d71b4e0837fff574c00e49176051a1c532d26c052a1e43231f252d813b"},
|
||||
{file = "grpcio-1.62.1-cp312-cp312-macosx_10_10_universal2.whl", hash = "sha256:d1f6c96573dc09d50dbcbd91dbf71d5cf97640c9427c32584010fbbd4c0e0037"},
|
||||
{file = "grpcio-1.62.1-cp312-cp312-manylinux_2_17_aarch64.whl", hash = "sha256:359f821d4578f80f41909b9ee9b76fb249a21035a061a327f91c953493782c31"},
|
||||
{file = "grpcio-1.62.1-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a485f0c2010c696be269184bdb5ae72781344cb4e60db976c59d84dd6354fac9"},
|
||||
{file = "grpcio-1.62.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b50b09b4dc01767163d67e1532f948264167cd27f49e9377e3556c3cba1268e1"},
|
||||
{file = "grpcio-1.62.1-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:3227c667dccbe38f2c4d943238b887bac588d97c104815aecc62d2fd976e014b"},
|
||||
{file = "grpcio-1.62.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:3952b581eb121324853ce2b191dae08badb75cd493cb4e0243368aa9e61cfd41"},
|
||||
{file = "grpcio-1.62.1-cp312-cp312-win32.whl", hash = "sha256:83a17b303425104d6329c10eb34bba186ffa67161e63fa6cdae7776ff76df73f"},
|
||||
{file = "grpcio-1.62.1-cp312-cp312-win_amd64.whl", hash = "sha256:6696ffe440333a19d8d128e88d440f91fb92c75a80ce4b44d55800e656a3ef1d"},
|
||||
{file = "grpcio-1.62.1-cp37-cp37m-linux_armv7l.whl", hash = "sha256:e3393b0823f938253370ebef033c9fd23d27f3eae8eb9a8f6264900c7ea3fb5a"},
|
||||
{file = "grpcio-1.62.1-cp37-cp37m-macosx_10_10_universal2.whl", hash = "sha256:83e7ccb85a74beaeae2634f10eb858a0ed1a63081172649ff4261f929bacfd22"},
|
||||
{file = "grpcio-1.62.1-cp37-cp37m-manylinux_2_17_aarch64.whl", hash = "sha256:882020c87999d54667a284c7ddf065b359bd00251fcd70279ac486776dbf84ec"},
|
||||
{file = "grpcio-1.62.1-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a10383035e864f386fe096fed5c47d27a2bf7173c56a6e26cffaaa5a361addb1"},
|
||||
{file = "grpcio-1.62.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:960edebedc6b9ada1ef58e1c71156f28689978188cd8cff3b646b57288a927d9"},
|
||||
{file = "grpcio-1.62.1-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:23e2e04b83f347d0aadde0c9b616f4726c3d76db04b438fd3904b289a725267f"},
|
||||
{file = "grpcio-1.62.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:978121758711916d34fe57c1f75b79cdfc73952f1481bb9583399331682d36f7"},
|
||||
{file = "grpcio-1.62.1-cp37-cp37m-win_amd64.whl", hash = "sha256:9084086190cc6d628f282e5615f987288b95457292e969b9205e45b442276407"},
|
||||
{file = "grpcio-1.62.1-cp38-cp38-linux_armv7l.whl", hash = "sha256:22bccdd7b23c420a27fd28540fb5dcbc97dc6be105f7698cb0e7d7a420d0e362"},
|
||||
{file = "grpcio-1.62.1-cp38-cp38-macosx_10_10_universal2.whl", hash = "sha256:8999bf1b57172dbc7c3e4bb3c732658e918f5c333b2942243f10d0d653953ba9"},
|
||||
{file = "grpcio-1.62.1-cp38-cp38-manylinux_2_17_aarch64.whl", hash = "sha256:d9e52558b8b8c2f4ac05ac86344a7417ccdd2b460a59616de49eb6933b07a0bd"},
|
||||
{file = "grpcio-1.62.1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1714e7bc935780bc3de1b3fcbc7674209adf5208ff825799d579ffd6cd0bd505"},
|
||||
{file = "grpcio-1.62.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c8842ccbd8c0e253c1f189088228f9b433f7a93b7196b9e5b6f87dba393f5d5d"},
|
||||
{file = "grpcio-1.62.1-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:1f1e7b36bdff50103af95a80923bf1853f6823dd62f2d2a2524b66ed74103e49"},
|
||||
{file = "grpcio-1.62.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:bba97b8e8883a8038606480d6b6772289f4c907f6ba780fa1f7b7da7dfd76f06"},
|
||||
{file = "grpcio-1.62.1-cp38-cp38-win32.whl", hash = "sha256:a7f615270fe534548112a74e790cd9d4f5509d744dd718cd442bf016626c22e4"},
|
||||
{file = "grpcio-1.62.1-cp38-cp38-win_amd64.whl", hash = "sha256:e6c8c8693df718c5ecbc7babb12c69a4e3677fd11de8886f05ab22d4e6b1c43b"},
|
||||
{file = "grpcio-1.62.1-cp39-cp39-linux_armv7l.whl", hash = "sha256:73db2dc1b201d20ab7083e7041946910bb991e7e9761a0394bbc3c2632326483"},
|
||||
{file = "grpcio-1.62.1-cp39-cp39-macosx_10_10_universal2.whl", hash = "sha256:407b26b7f7bbd4f4751dbc9767a1f0716f9fe72d3d7e96bb3ccfc4aace07c8de"},
|
||||
{file = "grpcio-1.62.1-cp39-cp39-manylinux_2_17_aarch64.whl", hash = "sha256:f8de7c8cef9261a2d0a62edf2ccea3d741a523c6b8a6477a340a1f2e417658de"},
|
||||
{file = "grpcio-1.62.1-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9bd5c8a1af40ec305d001c60236308a67e25419003e9bb3ebfab5695a8d0b369"},
|
||||
{file = "grpcio-1.62.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:be0477cb31da67846a33b1a75c611f88bfbcd427fe17701b6317aefceee1b96f"},
|
||||
{file = "grpcio-1.62.1-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:60dcd824df166ba266ee0cfaf35a31406cd16ef602b49f5d4dfb21f014b0dedd"},
|
||||
{file = "grpcio-1.62.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:973c49086cabab773525f6077f95e5a993bfc03ba8fc32e32f2c279497780585"},
|
||||
{file = "grpcio-1.62.1-cp39-cp39-win32.whl", hash = "sha256:12859468e8918d3bd243d213cd6fd6ab07208195dc140763c00dfe901ce1e1b4"},
|
||||
{file = "grpcio-1.62.1-cp39-cp39-win_amd64.whl", hash = "sha256:b7209117bbeebdfa5d898205cc55153a51285757902dd73c47de498ad4d11332"},
|
||||
{file = "grpcio-1.62.1.tar.gz", hash = "sha256:6c455e008fa86d9e9a9d85bb76da4277c0d7d9668a3bfa70dbe86e9f3c759947"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
protobuf = ["grpcio-tools (>=1.62.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "gym"
|
||||
version = "0.26.2"
|
||||
|
|
@ -1012,13 +1088,13 @@ setuptools = "*"
|
|||
|
||||
[[package]]
|
||||
name = "importlib-metadata"
|
||||
version = "7.0.2"
|
||||
version = "7.1.0"
|
||||
description = "Read metadata from Python packages"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "importlib_metadata-7.0.2-py3-none-any.whl", hash = "sha256:f4bc4c0c070c490abf4ce96d715f68e95923320370efb66143df00199bb6c100"},
|
||||
{file = "importlib_metadata-7.0.2.tar.gz", hash = "sha256:198f568f3230878cb1b44fbd7975f87906c22336dba2e4a7f05278c281fbd792"},
|
||||
{file = "importlib_metadata-7.1.0-py3-none-any.whl", hash = "sha256:30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570"},
|
||||
{file = "importlib_metadata-7.1.0.tar.gz", hash = "sha256:b78938b926ee8d5f020fc4772d487045805a55ddbad2ecf21c6d60938dc7fcd2"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1027,17 +1103,17 @@ zipp = ">=0.5"
|
|||
[package.extras]
|
||||
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
|
||||
perf = ["ipython"]
|
||||
testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
|
||||
testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "importlib-resources"
|
||||
version = "6.3.0"
|
||||
version = "6.3.2"
|
||||
description = "Read resources from Python packages"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "importlib_resources-6.3.0-py3-none-any.whl", hash = "sha256:783407aa1cd05550e3aa123e8f7cfaebee35ffa9cb0242919e2d1e4172222705"},
|
||||
{file = "importlib_resources-6.3.0.tar.gz", hash = "sha256:166072a97e86917a9025876f34286f549b9caf1d10b35a1b372bffa1600c6569"},
|
||||
{file = "importlib_resources-6.3.2-py3-none-any.whl", hash = "sha256:f41f4098b16cd140a97d256137cfd943d958219007990b2afb00439fc623f580"},
|
||||
{file = "importlib_resources-6.3.2.tar.gz", hash = "sha256:963eb79649252b0160c1afcfe5a1d3fe3ad66edd0a8b114beacffb70c0674223"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -1254,6 +1330,21 @@ html5 = ["html5lib"]
|
|||
htmlsoup = ["BeautifulSoup4"]
|
||||
source = ["Cython (>=3.0.7)"]
|
||||
|
||||
[[package]]
|
||||
name = "markdown"
|
||||
version = "3.6"
|
||||
description = "Python implementation of John Gruber's Markdown."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "Markdown-3.6-py3-none-any.whl", hash = "sha256:48f276f4d8cfb8ce6527c8f79e2ee29708508bf4d40aa410fbc3b4ee832c850f"},
|
||||
{file = "Markdown-3.6.tar.gz", hash = "sha256:ed4f41f6daecbeeb96e576ce414c41d2d876daa9a16cb35fa8ed8c2ddfad0224"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"]
|
||||
testing = ["coverage", "pyyaml"]
|
||||
|
||||
[[package]]
|
||||
name = "markupsafe"
|
||||
version = "2.1.5"
|
||||
|
|
@ -1459,32 +1550,32 @@ setuptools = "*"
|
|||
|
||||
[[package]]
|
||||
name = "numba"
|
||||
version = "0.59.0"
|
||||
version = "0.59.1"
|
||||
description = "compiling Python code using LLVM"
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optional = false
|
||||
python-versions = ">=3.9"
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{file = "numba-0.59.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7d80bce4ef7e65bf895c29e3889ca75a29ee01da80266a01d34815918e365835"},
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||||
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||||
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||||
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|
||||
{file = "numba-0.59.1-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:649913a3758891c77c32e2d2a3bcbedf4a69f5fea276d11f9119677c45a422e8"},
|
||||
{file = "numba-0.59.1-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:9712808e4545270291d76b9a264839ac878c5eb7d8b6e02c970dc0ac29bc8187"},
|
||||
{file = "numba-0.59.1-cp39-cp39-win_amd64.whl", hash = "sha256:8d51ccd7008a83105ad6a0082b6a2b70f1142dc7cfd76deb8c5a862367eb8c86"},
|
||||
{file = "numba-0.59.1.tar.gz", hash = "sha256:76f69132b96028d2774ed20415e8c528a34e3299a40581bae178f0994a2f370b"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2310,6 +2401,30 @@ urllib3 = ">=1.21.1,<3"
|
|||
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
|
||||
use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
|
||||
|
||||
[[package]]
|
||||
name = "robomimic"
|
||||
version = "0.2.0"
|
||||
description = "robomimic: A Modular Framework for Robot Learning from Demonstration"
|
||||
optional = false
|
||||
python-versions = ">=3"
|
||||
files = [
|
||||
{file = "robomimic-0.2.0.tar.gz", hash = "sha256:ee3bb5cf9c3e1feead6b57b43c5db738fd0a8e0c015fdf6419808af8fffdc463"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
egl_probe = ">=1.0.1"
|
||||
h5py = "*"
|
||||
imageio = "*"
|
||||
imageio-ffmpeg = "*"
|
||||
numpy = ">=1.13.3"
|
||||
psutil = "*"
|
||||
tensorboard = "*"
|
||||
tensorboardX = "*"
|
||||
termcolor = "*"
|
||||
torch = "*"
|
||||
torchvision = "*"
|
||||
tqdm = "*"
|
||||
|
||||
[[package]]
|
||||
name = "safetensors"
|
||||
version = "0.4.2"
|
||||
|
|
@ -2534,13 +2649,13 @@ test = ["asv", "gmpy2", "hypothesis", "mpmath", "pooch", "pytest", "pytest-cov",
|
|||
|
||||
[[package]]
|
||||
name = "sentry-sdk"
|
||||
version = "1.42.0"
|
||||
version = "1.43.0"
|
||||
description = "Python client for Sentry (https://sentry.io)"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "sentry-sdk-1.42.0.tar.gz", hash = "sha256:4a8364b8f7edbf47f95f7163e48334c96100d9c098f0ae6606e2e18183c223e6"},
|
||||
{file = "sentry_sdk-1.42.0-py2.py3-none-any.whl", hash = "sha256:a654ee7e497a3f5f6368b36d4f04baeab1fe92b3105f7f6965d6ef0de35a9ba4"},
|
||||
{file = "sentry-sdk-1.43.0.tar.gz", hash = "sha256:41df73af89d22921d8733714fb0fc5586c3461907e06688e6537d01a27e0e0f6"},
|
||||
{file = "sentry_sdk-1.43.0-py2.py3-none-any.whl", hash = "sha256:8d768724839ca18d7b4c7463ef7528c40b7aa2bfbf7fe554d5f9a7c044acfd36"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2554,6 +2669,7 @@ asyncpg = ["asyncpg (>=0.23)"]
|
|||
beam = ["apache-beam (>=2.12)"]
|
||||
bottle = ["bottle (>=0.12.13)"]
|
||||
celery = ["celery (>=3)"]
|
||||
celery-redbeat = ["celery-redbeat (>=2)"]
|
||||
chalice = ["chalice (>=1.16.0)"]
|
||||
clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
|
||||
django = ["django (>=1.8)"]
|
||||
|
|
@ -2798,9 +2914,58 @@ files = [
|
|||
[package.dependencies]
|
||||
mpmath = ">=0.19"
|
||||
|
||||
[[package]]
|
||||
name = "tensorboard"
|
||||
version = "2.16.2"
|
||||
description = "TensorBoard lets you watch Tensors Flow"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "tensorboard-2.16.2-py3-none-any.whl", hash = "sha256:9f2b4e7dad86667615c0e5cd072f1ea8403fc032a299f0072d6f74855775cc45"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
absl-py = ">=0.4"
|
||||
grpcio = ">=1.48.2"
|
||||
markdown = ">=2.6.8"
|
||||
numpy = ">=1.12.0"
|
||||
protobuf = ">=3.19.6,<4.24.0 || >4.24.0"
|
||||
setuptools = ">=41.0.0"
|
||||
six = ">1.9"
|
||||
tensorboard-data-server = ">=0.7.0,<0.8.0"
|
||||
werkzeug = ">=1.0.1"
|
||||
|
||||
[[package]]
|
||||
name = "tensorboard-data-server"
|
||||
version = "0.7.2"
|
||||
description = "Fast data loading for TensorBoard"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb"},
|
||||
{file = "tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60"},
|
||||
{file = "tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:ef687163c24185ae9754ed5650eb5bc4d84ff257aabdc33f0cc6f74d8ba54530"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tensorboardx"
|
||||
version = "2.6.2.2"
|
||||
description = "TensorBoardX lets you watch Tensors Flow without Tensorflow"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "tensorboardX-2.6.2.2-py2.py3-none-any.whl", hash = "sha256:160025acbf759ede23fd3526ae9d9bfbfd8b68eb16c38a010ebe326dc6395db8"},
|
||||
{file = "tensorboardX-2.6.2.2.tar.gz", hash = "sha256:c6476d7cd0d529b0b72f4acadb1269f9ed8b22f441e87a84f2a3b940bb87b666"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = "*"
|
||||
packaging = "*"
|
||||
protobuf = ">=3.20"
|
||||
|
||||
[[package]]
|
||||
name = "tensordict"
|
||||
version = "0.4.0+6a56ecd"
|
||||
version = "0.4.0+b4c91e8"
|
||||
description = ""
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
|
|
@ -2821,7 +2986,7 @@ tests = ["pytest", "pytest-benchmark", "pytest-instafail", "pytest-rerunfailures
|
|||
type = "git"
|
||||
url = "https://github.com/pytorch/tensordict"
|
||||
reference = "HEAD"
|
||||
resolved_reference = "6a56ecd728757feee387f946b7da66dd452b739b"
|
||||
resolved_reference = "b4c91e8828c538ca0a50d8383fd99311a9afb078"
|
||||
|
||||
[[package]]
|
||||
name = "termcolor"
|
||||
|
|
@ -3084,6 +3249,23 @@ perf = ["orjson"]
|
|||
reports = ["pydantic (>=2.0.0)"]
|
||||
sweeps = ["sweeps (>=0.2.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "werkzeug"
|
||||
version = "3.0.1"
|
||||
description = "The comprehensive WSGI web application library."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "werkzeug-3.0.1-py3-none-any.whl", hash = "sha256:90a285dc0e42ad56b34e696398b8122ee4c681833fb35b8334a095d82c56da10"},
|
||||
{file = "werkzeug-3.0.1.tar.gz", hash = "sha256:507e811ecea72b18a404947aded4b3390e1db8f826b494d76550ef45bb3b1dcc"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
MarkupSafe = ">=2.1.1"
|
||||
|
||||
[package.extras]
|
||||
watchdog = ["watchdog (>=2.3)"]
|
||||
|
||||
[[package]]
|
||||
name = "zarr"
|
||||
version = "2.17.1"
|
||||
|
|
@ -3107,13 +3289,13 @@ jupyter = ["ipytree (>=0.2.2)", "ipywidgets (>=8.0.0)", "notebook"]
|
|||
|
||||
[[package]]
|
||||
name = "zipp"
|
||||
version = "3.18.0"
|
||||
version = "3.18.1"
|
||||
description = "Backport of pathlib-compatible object wrapper for zip files"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "zipp-3.18.0-py3-none-any.whl", hash = "sha256:c1bb803ed69d2cce2373152797064f7e79bc43f0a3748eb494096a867e0ebf79"},
|
||||
{file = "zipp-3.18.0.tar.gz", hash = "sha256:df8d042b02765029a09b157efd8e820451045890acc30f8e37dd2f94a060221f"},
|
||||
{file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
|
||||
{file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -3123,4 +3305,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.10"
|
||||
content-hash = "4aa6a1e3f29560dd4a1c24d493ee1154089da4aa8d2190ad1f786c125ab2b735"
|
||||
content-hash = "cbd9aedcb3a24417b85124fb94db706dd6ca0a90dfb610b0aebdcd3aa2a0333c"
|
||||
|
|
|
|||
|
|
@ -51,6 +51,7 @@ torchvision = {version = "^0.17.1", source = "torch-cpu"}
|
|||
h5py = "^3.10.0"
|
||||
dm = "^1.3"
|
||||
dm-control = "^1.0.16"
|
||||
robomimic = "0.2.0"
|
||||
huggingface-hub = "^0.21.4"
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -192,7 +192,7 @@ class AlohaEnv(AbstractEnv):
|
|||
{
|
||||
"observation": TensorDict(obs, batch_size=[]),
|
||||
"reward": torch.tensor([reward], dtype=torch.float32),
|
||||
# succes and done are true when coverage > self.success_threshold in env
|
||||
# success and done are true when coverage > self.success_threshold in env
|
||||
"done": torch.tensor([done], dtype=torch.bool),
|
||||
"success": torch.tensor([success], dtype=torch.bool),
|
||||
},
|
||||
|
|
|
|||
|
|
@ -62,27 +62,3 @@ def make_env(cfg, transform=None):
|
|||
{"seed": env_seed} for env_seed in range(cfg.seed, cfg.seed + cfg.rollout_batch_size)
|
||||
],
|
||||
)
|
||||
|
||||
|
||||
# def make_env(env_name, frame_skip, device, is_test=False):
|
||||
# env = GymEnv(
|
||||
# env_name,
|
||||
# frame_skip=frame_skip,
|
||||
# from_pixels=True,
|
||||
# pixels_only=False,
|
||||
# device=device,
|
||||
# )
|
||||
# env = TransformedEnv(env)
|
||||
# env.append_transform(NoopResetEnv(noops=30, random=True))
|
||||
# if not is_test:
|
||||
# env.append_transform(EndOfLifeTransform())
|
||||
# env.append_transform(RewardClipping(-1, 1))
|
||||
# env.append_transform(ToTensorImage())
|
||||
# env.append_transform(GrayScale())
|
||||
# env.append_transform(Resize(84, 84))
|
||||
# env.append_transform(CatFrames(N=4, dim=-3))
|
||||
# env.append_transform(RewardSum())
|
||||
# env.append_transform(StepCounter(max_steps=4500))
|
||||
# env.append_transform(DoubleToFloat())
|
||||
# env.append_transform(VecNorm(in_keys=["pixels"]))
|
||||
# return env
|
||||
|
|
|
|||
|
|
@ -3,6 +3,8 @@ import logging
|
|||
from collections import deque
|
||||
from typing import Optional
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import torch
|
||||
from tensordict import TensorDict
|
||||
from torchrl.data.tensor_specs import (
|
||||
|
|
@ -59,12 +61,30 @@ class PushtEnv(AbstractEnv):
|
|||
|
||||
self._env = PushTImageEnv(render_size=self.image_size)
|
||||
|
||||
def render(self, mode="rgb_array", width=384, height=384):
|
||||
def render(self, mode="rgb_array", width=96, height=96, with_marker=True):
|
||||
"""
|
||||
with_marker adds a cursor showing the targeted action for the controller.
|
||||
"""
|
||||
if width != height:
|
||||
raise NotImplementedError()
|
||||
tmp = self._env.render_size
|
||||
self._env.render_size = width
|
||||
out = self._env.render(mode)
|
||||
if width != self._env.render_size:
|
||||
self._env.render_cache = None
|
||||
self._env.render_size = width
|
||||
out = self._env.render(mode).copy()
|
||||
if with_marker and self._env.latest_action is not None:
|
||||
action = np.array(self._env.latest_action)
|
||||
coord = (action / 512 * self._env.render_size).astype(np.int32)
|
||||
marker_size = int(8 / 96 * self._env.render_size)
|
||||
thickness = int(1 / 96 * self._env.render_size)
|
||||
cv2.drawMarker(
|
||||
out,
|
||||
coord,
|
||||
color=(255, 0, 0),
|
||||
markerType=cv2.MARKER_CROSS,
|
||||
markerSize=marker_size,
|
||||
thickness=thickness,
|
||||
)
|
||||
self._env.render_size = tmp
|
||||
return out
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,3 @@
|
|||
import cv2
|
||||
import numpy as np
|
||||
from gym import spaces
|
||||
|
||||
|
|
@ -28,20 +27,6 @@ class PushTImageEnv(PushTEnv):
|
|||
img_obs = np.moveaxis(img, -1, 0)
|
||||
obs = {"image": img_obs, "agent_pos": agent_pos}
|
||||
|
||||
# draw action
|
||||
if self.latest_action is not None:
|
||||
action = np.array(self.latest_action)
|
||||
coord = (action / 512 * 96).astype(np.int32)
|
||||
marker_size = int(8 / 96 * self.render_size)
|
||||
thickness = int(1 / 96 * self.render_size)
|
||||
cv2.drawMarker(
|
||||
img,
|
||||
coord,
|
||||
color=(255, 0, 0),
|
||||
markerType=cv2.MARKER_CROSS,
|
||||
markerSize=marker_size,
|
||||
thickness=thickness,
|
||||
)
|
||||
self.render_cache = img
|
||||
|
||||
return obs
|
||||
|
|
|
|||
|
|
@ -1,3 +1,44 @@
|
|||
"""Code from the original diffusion policy project.
|
||||
|
||||
Notes on how to load a checkpoint from the original repository:
|
||||
|
||||
In the original repository, run the eval and use a breakpoint to extract the policy weights.
|
||||
|
||||
```
|
||||
torch.save(policy.state_dict(), "weights.pt")
|
||||
```
|
||||
|
||||
In this repository, add a breakpoint somewhere after creating an equivalent policy and load in the weights:
|
||||
|
||||
```
|
||||
loaded = torch.load("weights.pt")
|
||||
aligned = {}
|
||||
their_prefix = "obs_encoder.obs_nets.image.backbone"
|
||||
our_prefix = "obs_encoder.key_model_map.image.backbone"
|
||||
aligned.update({our_prefix + k.removeprefix(their_prefix): v for k, v in loaded.items() if k.startswith(their_prefix)})
|
||||
their_prefix = "obs_encoder.obs_nets.image.pool"
|
||||
our_prefix = "obs_encoder.key_model_map.image.pool"
|
||||
aligned.update({our_prefix + k.removeprefix(their_prefix): v for k, v in loaded.items() if k.startswith(their_prefix)})
|
||||
their_prefix = "obs_encoder.obs_nets.image.nets.3"
|
||||
our_prefix = "obs_encoder.key_model_map.image.out"
|
||||
aligned.update({our_prefix + k.removeprefix(their_prefix): v for k, v in loaded.items() if k.startswith(their_prefix)})
|
||||
aligned.update({k: v for k, v in loaded.items() if k.startswith('model.')})
|
||||
# Note: here you are loading into the ema model.
|
||||
missing_keys, unexpected_keys = policy.ema_diffusion.load_state_dict(aligned, strict=False)
|
||||
assert all('_dummy_variable' in k for k in missing_keys)
|
||||
assert len(unexpected_keys) == 0
|
||||
```
|
||||
|
||||
Then in that same runtime you can also save the weights with the new aligned state_dict:
|
||||
|
||||
```
|
||||
policy.save("weights.pt")
|
||||
```
|
||||
|
||||
Now you can remove the breakpoint and extra code and load in the weights just like with any other lerobot checkpoint.
|
||||
|
||||
"""
|
||||
|
||||
from typing import Dict
|
||||
|
||||
import torch
|
||||
|
|
@ -190,11 +231,10 @@ class DiffusionUnetImagePolicy(BaseImagePolicy):
|
|||
|
||||
# run sampling
|
||||
nsample = self.conditional_sample(
|
||||
cond_data, cond_mask, local_cond=local_cond, global_cond=global_cond, **self.kwargs
|
||||
cond_data, cond_mask, local_cond=local_cond, global_cond=global_cond
|
||||
)
|
||||
|
||||
action_pred = nsample[..., :action_dim]
|
||||
|
||||
# get action
|
||||
start = n_obs_steps - 1
|
||||
end = start + self.n_action_steps
|
||||
|
|
|
|||
|
|
@ -1,15 +1,40 @@
|
|||
import copy
|
||||
from typing import Dict, Tuple, Union
|
||||
from typing import Dict, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torchvision
|
||||
from robomimic.models.base_nets import ResNet18Conv, SpatialSoftmax
|
||||
|
||||
from lerobot.common.policies.diffusion.model.crop_randomizer import CropRandomizer
|
||||
from lerobot.common.policies.diffusion.model.module_attr_mixin import ModuleAttrMixin
|
||||
from lerobot.common.policies.diffusion.pytorch_utils import replace_submodules
|
||||
|
||||
|
||||
class RgbEncoder(nn.Module):
|
||||
"""Following `VisualCore` from Robomimic 0.2.0."""
|
||||
|
||||
def __init__(self, input_shape, relu=True, pretrained=False, num_keypoints=32):
|
||||
"""
|
||||
input_shape: channel-first input shape (C, H, W)
|
||||
resnet_name: a timm model name.
|
||||
pretrained: whether to use timm pretrained weights.
|
||||
relu: whether to use relu as a final step.
|
||||
num_keypoints: Number of keypoints for SpatialSoftmax (default value of 32 matches PushT Image).
|
||||
"""
|
||||
super().__init__()
|
||||
self.backbone = ResNet18Conv(input_channel=input_shape[0], pretrained=pretrained)
|
||||
# Figure out the feature map shape.
|
||||
with torch.inference_mode():
|
||||
feat_map_shape = tuple(self.backbone(torch.zeros(size=(1, *input_shape))).shape[1:])
|
||||
self.pool = SpatialSoftmax(feat_map_shape, num_kp=num_keypoints)
|
||||
self.out = nn.Linear(num_keypoints * 2, num_keypoints * 2)
|
||||
self.relu = nn.ReLU() if relu else nn.Identity()
|
||||
|
||||
def forward(self, x):
|
||||
return self.relu(self.out(torch.flatten(self.pool(self.backbone(x)), start_dim=1)))
|
||||
|
||||
|
||||
class MultiImageObsEncoder(ModuleAttrMixin):
|
||||
def __init__(
|
||||
self,
|
||||
|
|
@ -24,7 +49,7 @@ class MultiImageObsEncoder(ModuleAttrMixin):
|
|||
share_rgb_model: bool = False,
|
||||
# renormalize rgb input with imagenet normalization
|
||||
# assuming input in [0,1]
|
||||
imagenet_norm: bool = False,
|
||||
norm_mean_std: Optional[tuple[float, float]] = None,
|
||||
):
|
||||
"""
|
||||
Assumes rgb input: B,C,H,W
|
||||
|
|
@ -98,10 +123,9 @@ class MultiImageObsEncoder(ModuleAttrMixin):
|
|||
this_normalizer = torchvision.transforms.CenterCrop(size=(h, w))
|
||||
# configure normalizer
|
||||
this_normalizer = nn.Identity()
|
||||
if imagenet_norm:
|
||||
# TODO(rcadene): move normalizer to dataset and env
|
||||
if norm_mean_std is not None:
|
||||
this_normalizer = torchvision.transforms.Normalize(
|
||||
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
|
||||
mean=norm_mean_std[0], std=norm_mean_std[1]
|
||||
)
|
||||
|
||||
this_transform = nn.Sequential(this_resizer, this_randomizer, this_normalizer)
|
||||
|
|
@ -124,6 +148,17 @@ class MultiImageObsEncoder(ModuleAttrMixin):
|
|||
def forward(self, obs_dict):
|
||||
batch_size = None
|
||||
features = []
|
||||
|
||||
# process lowdim input
|
||||
for key in self.low_dim_keys:
|
||||
data = obs_dict[key]
|
||||
if batch_size is None:
|
||||
batch_size = data.shape[0]
|
||||
else:
|
||||
assert batch_size == data.shape[0]
|
||||
assert data.shape[1:] == self.key_shape_map[key]
|
||||
features.append(data)
|
||||
|
||||
# process rgb input
|
||||
if self.share_rgb_model:
|
||||
# pass all rgb obs to rgb model
|
||||
|
|
@ -161,16 +196,6 @@ class MultiImageObsEncoder(ModuleAttrMixin):
|
|||
feature = self.key_model_map[key](img)
|
||||
features.append(feature)
|
||||
|
||||
# process lowdim input
|
||||
for key in self.low_dim_keys:
|
||||
data = obs_dict[key]
|
||||
if batch_size is None:
|
||||
batch_size = data.shape[0]
|
||||
else:
|
||||
assert batch_size == data.shape[0]
|
||||
assert data.shape[1:] == self.key_shape_map[key]
|
||||
features.append(data)
|
||||
|
||||
# concatenate all features
|
||||
result = torch.cat(features, dim=-1)
|
||||
return result
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
import copy
|
||||
import logging
|
||||
import time
|
||||
|
||||
import hydra
|
||||
|
|
@ -7,7 +8,7 @@ import torch
|
|||
from lerobot.common.policies.abstract import AbstractPolicy
|
||||
from lerobot.common.policies.diffusion.diffusion_unet_image_policy import DiffusionUnetImagePolicy
|
||||
from lerobot.common.policies.diffusion.model.lr_scheduler import get_scheduler
|
||||
from lerobot.common.policies.diffusion.model.multi_image_obs_encoder import MultiImageObsEncoder
|
||||
from lerobot.common.policies.diffusion.model.multi_image_obs_encoder import MultiImageObsEncoder, RgbEncoder
|
||||
from lerobot.common.utils import get_safe_torch_device
|
||||
|
||||
|
||||
|
|
@ -39,7 +40,10 @@ class DiffusionPolicy(AbstractPolicy):
|
|||
self.cfg = cfg
|
||||
|
||||
noise_scheduler = hydra.utils.instantiate(cfg_noise_scheduler)
|
||||
rgb_model = hydra.utils.instantiate(cfg_rgb_model)
|
||||
rgb_model_input_shape = copy.deepcopy(shape_meta.obs.image.shape)
|
||||
if cfg_obs_encoder.crop_shape is not None:
|
||||
rgb_model_input_shape[1:] = cfg_obs_encoder.crop_shape
|
||||
rgb_model = RgbEncoder(input_shape=rgb_model_input_shape, **cfg_rgb_model)
|
||||
obs_encoder = MultiImageObsEncoder(
|
||||
rgb_model=rgb_model,
|
||||
**cfg_obs_encoder,
|
||||
|
|
@ -66,11 +70,13 @@ class DiffusionPolicy(AbstractPolicy):
|
|||
self.device = get_safe_torch_device(cfg_device)
|
||||
self.diffusion.to(self.device)
|
||||
|
||||
self.ema_diffusion = None
|
||||
self.ema = None
|
||||
if self.cfg.use_ema:
|
||||
self.ema_diffusion = copy.deepcopy(self.diffusion)
|
||||
self.ema = hydra.utils.instantiate(
|
||||
cfg_ema,
|
||||
model=copy.deepcopy(self.diffusion),
|
||||
model=self.ema_diffusion,
|
||||
)
|
||||
|
||||
self.optimizer = hydra.utils.instantiate(
|
||||
|
|
@ -94,6 +100,9 @@ class DiffusionPolicy(AbstractPolicy):
|
|||
|
||||
@torch.no_grad()
|
||||
def select_actions(self, observation, step_count):
|
||||
"""
|
||||
Note: this uses the ema model weights if self.training == False, otherwise the non-ema model weights.
|
||||
"""
|
||||
# TODO(rcadene): remove unused step_count
|
||||
del step_count
|
||||
|
||||
|
|
@ -101,7 +110,10 @@ class DiffusionPolicy(AbstractPolicy):
|
|||
"image": observation["image"],
|
||||
"agent_pos": observation["state"],
|
||||
}
|
||||
out = self.diffusion.predict_action(obs_dict)
|
||||
if self.training:
|
||||
out = self.diffusion.predict_action(obs_dict)
|
||||
else:
|
||||
out = self.ema_diffusion.predict_action(obs_dict)
|
||||
action = out["action"]
|
||||
return action
|
||||
|
||||
|
|
@ -191,4 +203,10 @@ class DiffusionPolicy(AbstractPolicy):
|
|||
|
||||
def load(self, fp):
|
||||
d = torch.load(fp)
|
||||
self.load_state_dict(d)
|
||||
missing_keys, unexpected_keys = self.load_state_dict(d, strict=False)
|
||||
if len(missing_keys) > 0:
|
||||
assert all(k.startswith("ema_diffusion.") for k in missing_keys)
|
||||
logging.warning(
|
||||
"DiffusionPolicy.load expected ema parameters in loaded state dict but none were found."
|
||||
)
|
||||
assert len(unexpected_keys) == 0
|
||||
|
|
|
|||
|
|
@ -12,6 +12,7 @@ shape_meta:
|
|||
action:
|
||||
shape: [2]
|
||||
|
||||
seed: 100000
|
||||
horizon: 16
|
||||
n_obs_steps: 2
|
||||
n_action_steps: 8
|
||||
|
|
@ -21,12 +22,12 @@ past_action_visible: False
|
|||
keypoint_visible_rate: 1.0
|
||||
obs_as_global_cond: True
|
||||
|
||||
eval_episodes: 1
|
||||
eval_freq: 10000
|
||||
save_freq: 100000
|
||||
eval_episodes: 50
|
||||
eval_freq: 5000
|
||||
save_freq: 5000
|
||||
log_freq: 250
|
||||
|
||||
offline_steps: 1344000
|
||||
offline_steps: 200000
|
||||
online_steps: 0
|
||||
|
||||
offline_prioritized_sampler: true
|
||||
|
|
@ -42,8 +43,8 @@ policy:
|
|||
num_inference_steps: 100
|
||||
obs_as_global_cond: ${obs_as_global_cond}
|
||||
# crop_shape: null
|
||||
diffusion_step_embed_dim: 256 # before 128
|
||||
down_dims: [256, 512, 1024] # before [512, 1024, 2048]
|
||||
diffusion_step_embed_dim: 128
|
||||
down_dims: [512, 1024, 2048]
|
||||
kernel_size: 5
|
||||
n_groups: 8
|
||||
cond_predict_scale: True
|
||||
|
|
@ -76,17 +77,17 @@ noise_scheduler:
|
|||
obs_encoder:
|
||||
shape_meta: ${shape_meta}
|
||||
# resize_shape: null
|
||||
# crop_shape: [76, 76]
|
||||
crop_shape: [84, 84]
|
||||
# constant center crop
|
||||
# random_crop: True
|
||||
random_crop: True
|
||||
use_group_norm: True
|
||||
share_rgb_model: False
|
||||
imagenet_norm: True
|
||||
norm_mean_std: [0.5, 0.5] # for PushT the original impl normalizes to [-1, 1] (maybe not the case for robomimic envs)
|
||||
|
||||
rgb_model:
|
||||
_target_: lerobot.common.policies.diffusion.pytorch_utils.get_resnet
|
||||
name: resnet18
|
||||
weights: null
|
||||
pretrained: false
|
||||
num_keypoints: 32
|
||||
relu: true
|
||||
|
||||
ema:
|
||||
_target_: lerobot.common.policies.diffusion.model.ema_model.EMAModel
|
||||
|
|
|
|||
|
|
@ -155,11 +155,7 @@ def train(cfg: dict, out_dir=None, job_name=None):
|
|||
num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad)
|
||||
num_total_params = sum(p.numel() for p in policy.parameters())
|
||||
|
||||
td_policy = TensorDictModule(
|
||||
policy,
|
||||
in_keys=["observation", "step_count"],
|
||||
out_keys=["action"],
|
||||
)
|
||||
td_policy = TensorDictModule(policy, in_keys=["observation", "step_count"], out_keys=["action"])
|
||||
|
||||
# log metrics to terminal and wandb
|
||||
logger = Logger(out_dir, job_name, cfg)
|
||||
|
|
@ -174,19 +170,9 @@ def train(cfg: dict, out_dir=None, job_name=None):
|
|||
logging.info(f"{num_learnable_params=} ({format_big_number(num_learnable_params)})")
|
||||
logging.info(f"{num_total_params=} ({format_big_number(num_total_params)})")
|
||||
|
||||
step = 0 # number of policy update (forward + backward + optim)
|
||||
|
||||
is_offline = True
|
||||
for offline_step in range(cfg.offline_steps):
|
||||
if offline_step == 0:
|
||||
logging.info("Start offline training on a fixed dataset")
|
||||
# TODO(rcadene): is it ok if step_t=0 = 0 and not 1 as previously done?
|
||||
policy.train()
|
||||
train_info = policy.update(offline_buffer, step)
|
||||
if step % cfg.log_freq == 0:
|
||||
log_train_info(logger, train_info, step, cfg, offline_buffer, is_offline)
|
||||
|
||||
if step > 0 and step % cfg.eval_freq == 0:
|
||||
# Note: this helper will be used in offline and online training loops.
|
||||
def _maybe_eval_and_maybe_save(step):
|
||||
if step % cfg.eval_freq == 0:
|
||||
logging.info(f"Eval policy at step {step}")
|
||||
eval_info, first_video = eval_policy(
|
||||
env,
|
||||
|
|
@ -202,11 +188,27 @@ def train(cfg: dict, out_dir=None, job_name=None):
|
|||
logger.log_video(first_video, step, mode="eval")
|
||||
logging.info("Resume training")
|
||||
|
||||
if step > 0 and cfg.save_model and step % cfg.save_freq == 0:
|
||||
logging.info(f"Checkpoint policy at step {step}")
|
||||
if cfg.save_model and step % cfg.save_freq == 0:
|
||||
logging.info(f"Checkpoint policy after step {step}")
|
||||
logger.save_model(policy, identifier=step)
|
||||
logging.info("Resume training")
|
||||
|
||||
step = 0 # number of policy update (forward + backward + optim)
|
||||
|
||||
is_offline = True
|
||||
for offline_step in range(cfg.offline_steps):
|
||||
if offline_step == 0:
|
||||
logging.info("Start offline training on a fixed dataset")
|
||||
# TODO(rcadene): is it ok if step_t=0 = 0 and not 1 as previously done?
|
||||
policy.train()
|
||||
train_info = policy.update(offline_buffer, step)
|
||||
if step % cfg.log_freq == 0:
|
||||
log_train_info(logger, train_info, step, cfg, offline_buffer, is_offline)
|
||||
|
||||
# Note: _maybe_eval_and_maybe_save happens **after** the `step`th training update has completed, so we pass in
|
||||
# step + 1.
|
||||
_maybe_eval_and_maybe_save(step + 1)
|
||||
|
||||
step += 1
|
||||
|
||||
demo_buffer = offline_buffer if cfg.policy.balanced_sampling else None
|
||||
|
|
@ -248,24 +250,9 @@ def train(cfg: dict, out_dir=None, job_name=None):
|
|||
train_info.update(rollout_info)
|
||||
log_train_info(logger, train_info, step, cfg, offline_buffer, is_offline)
|
||||
|
||||
if step > 0 and step % cfg.eval_freq == 0:
|
||||
logging.info(f"Eval policy at step {step}")
|
||||
eval_info, first_video = eval_policy(
|
||||
env,
|
||||
td_policy,
|
||||
num_episodes=cfg.eval_episodes,
|
||||
max_steps=cfg.env.episode_length // cfg.n_action_steps,
|
||||
return_first_video=True,
|
||||
)
|
||||
log_eval_info(logger, eval_info, step, cfg, offline_buffer, is_offline)
|
||||
if cfg.wandb.enable:
|
||||
logger.log_video(first_video, step, mode="eval")
|
||||
logging.info("Resume training")
|
||||
|
||||
if step > 0 and cfg.save_model and step % cfg.save_freq == 0:
|
||||
logging.info(f"Checkpoint policy at step {step}")
|
||||
logger.save_model(policy, identifier=step)
|
||||
logging.info("Resume training")
|
||||
# Note: _maybe_eval_and_maybe_save happens **after** the `step`th training update has completed, so we pass
|
||||
# in step + 1.
|
||||
_maybe_eval_and_maybe_save(step + 1)
|
||||
|
||||
step += 1
|
||||
online_step += 1
|
||||
|
|
|
|||
|
|
@ -44,56 +44,56 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "av"
|
||||
version = "11.0.0"
|
||||
version = "12.0.0"
|
||||
description = "Pythonic bindings for FFmpeg's libraries."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "av-11.0.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a01f13b37eb6d181e03bbbbda29093fe2d68f10755795188220acdc89560ec27"},
|
||||
{file = "av-11.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b2236faee1b5d71dff3cdef81ef6eec22cc8b71dbfb45eb037e6437fe80f24e7"},
|
||||
{file = "av-11.0.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:40543a08e5c84aecd2bc84da5d43548743201897f0ba21bf5ae3a4dcddefca2b"},
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||||
{file = "av-11.0.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2907376884d956376aaf3bc1905fa4e0dcb9ba4e0d183e519392a19d89317d1b"},
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||||
{file = "av-11.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e8d5581dcdc81cd601e3ce036809f14da82c46ff187bcefe981ec819390e0ab0"},
|
||||
{file = "av-11.0.0-cp310-cp310-win_amd64.whl", hash = "sha256:150490f2a62cfa470f3cb60f3a0060ff93afd807e2b7b3b0eeeb5a992eb8d67b"},
|
||||
{file = "av-11.0.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:d9bac0de62f09e2cb4e2132b5a46a89bc31c898189aa285b484c17351d991afe"},
|
||||
{file = "av-11.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2122ff8bdace4ce50207920f37de472517921e2ca1f0503464f748fdb8e20506"},
|
||||
{file = "av-11.0.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:527d840697fee6ad4cf47eba987eaf30cd76bd96b2d20eaa907e166b9b8065c8"},
|
||||
{file = "av-11.0.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:abeaedddfca9101886eb6fc47318c5f5ece8480d330d73aacf6917d7421981a2"},
|
||||
{file = "av-11.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:13790fbb889b955baf885fe3761e923e85537ef414173465ec293177cedb7b99"},
|
||||
{file = "av-11.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:fc27e27f52480287f44226ad4ae3eb53346bf027959d0f00a9154530bd98b371"},
|
||||
{file = "av-11.0.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:892583e2c6b8c2500e5d24310f499caefcdaa2e48c8f7169ad41041aaaf4da11"},
|
||||
{file = "av-11.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6943679d70a9f4de974049e7ae2cf0b20afe0d7ddab650526c02a6cf9adcd08f"},
|
||||
{file = "av-11.0.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e6d73b038ccf1df5c16bc643eee5c694fb7732e09375e2f4903c1f4ce90dfb72"},
|
||||
{file = "av-11.0.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c83422db3333e97b9680700df5185139352fc3a568b14179da3bdcbeb2f0e91b"},
|
||||
{file = "av-11.0.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8413900f6a3639e0088c018a3a516a1656d4d16799e7aa759a16ddf3bd268e2b"},
|
||||
{file = "av-11.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:908e49ee336223801d8f2f7dca5a1deb64e9d8256138b8e7a79013b682a6ebb5"},
|
||||
{file = "av-11.0.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:82411ae4a562da07b76028d2f349fb0e6a86aa78ad2b18d2d7bf5b06b17fba14"},
|
||||
{file = "av-11.0.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:621104bd63e38fa4eca554da3722b1aac329619de39152f27eec8999acc72342"},
|
||||
{file = "av-11.0.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:442878990c094455a16c10127edcc54bc4e78d355e6a13ad2a27608b0ecda38f"},
|
||||
{file = "av-11.0.0-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:658199c92987dc72511f5ee8ade62faef6234b7a04c8b5788de99e366be5e073"},
|
||||
{file = "av-11.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad4b381665c49267b46f87297573898b85e5c41384750fee2e70267fbc4ba318"},
|
||||
{file = "av-11.0.0-cp38-cp38-win_amd64.whl", hash = "sha256:60de14f71293e36ca4e297cc8a8460f0cf74f38a201694f3c6fc7f40301582f2"},
|
||||
{file = "av-11.0.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:a90f04af96374dab94028a7471597bdfcf03083338b9be2eb8ca4805a8ec7ab5"},
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||||
{file = "av-11.0.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:8821ab2d23e4cb5c8abea6b08d2b1bfceca6af2d88fab1d1dc1b3ec7b34933c7"},
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||||
{file = "av-11.0.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9a92342ed307eeaf9509a6b0f3bafd4337c4880c851b50acc18df48c625b63b6"},
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||||
{file = "av-11.0.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bbe3502975bc844f5d432c1f24d331bf6ef3e05532ebf06f7ed08b60719b8ea5"},
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||||
{file = "av-11.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c278b3a4fd111b4c9190abe6b1a5ca358d5f91e851d470b62577b957e0187b09"},
|
||||
{file = "av-11.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:478aa1d54fbc3058ea65ff41086b6adbe1326b456a027d2f3b59dbe60b4ac2ca"},
|
||||
{file = "av-11.0.0-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:e8df10bb2d56a981d02a8a0b41491912b76dad06305d174a2575ef55ad451100"},
|
||||
{file = "av-11.0.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b30c51e597785a89241bd61865faff2dbd3327856a8285a1e120dbf60e18348b"},
|
||||
{file = "av-11.0.0-pp310-pypy310_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a8b8bd92edb096699b306e7b090ad096925ca3bdae6f89656f023fa2a2da627d"},
|
||||
{file = "av-11.0.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9383af733abfc44f6fc29307a6c922fbf671ee343dc97b78b74eac6a2346a46d"},
|
||||
{file = "av-11.0.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:a9df4a60579198b560f641cdfe4c2139948a70193ddc096b275f2cf6d94e3e04"},
|
||||
{file = "av-11.0.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:8ae5f7ae0a7093fb813686d4aa4c554531f80a28480427f5c155da51b747eff0"},
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||||
{file = "av-11.0.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:50fb7d606f8236891d773c701d5650b93af8dbf78eeaac36fc7e1f7f64a9d664"},
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||||
{file = "av-11.0.0-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:543e0f9bf6ff02dedbe66d906fbc89c8907c80a8ea7413fc3fed68ce4a6e9b44"},
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||||
{file = "av-11.0.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:daa279c884457ab194ce78bdd89c0aa391af733da95fb3258d4c6eb8c258299a"},
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||||
{file = "av-11.0.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:1aacc21f4cf96447117a61edfb776afb73186750a5e08a21484ddfc3599aefb5"},
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||||
{file = "av-11.0.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2568b38eef777b916a5d02e42b8f67f92e12023531239ddd32e1ca4f3cdf8c5b"},
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||||
{file = "av-11.0.0-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:747c6d347e27c59cc2e78c9c505d23cd88eceff0cc9386be73693ae9009a577c"},
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||||
{file = "av-11.0.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4bbd8f4941b9d3450eff40003b9b9d904667aec7ab085fa31f0f9bca32d755e0"},
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||||
{file = "av-11.0.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:f39c1244ba0cf185b2722aeec116b8a98a2ee5728ce687cec0bda60ee0360dfc"},
|
||||
{file = "av-11.0.0.tar.gz", hash = "sha256:48223f000a252070f8e700ff634bb7fb3aa1b7bc7e450373029fbdd6f369ac31"},
|
||||
{file = "av-12.0.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b9d0890553951f76c479a9f2bb952aebae902b1c7d52feea614d37e1cd728a44"},
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||||
{file = "av-12.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5d7f229a253c2e3fea9682c09c5ae179bd6d5d2da38d89eb7f29ef7bed10cb2f"},
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||||
{file = "av-12.0.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:61b3555d143aacf02e0446f6030319403538eba4dc713c18dfa653a2a23e7f9c"},
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||||
{file = "av-12.0.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:607e13b2c2b26159a37525d7b6f647a32ce78711fccff23d146d3e255ffa115f"},
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||||
{file = "av-12.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:39f0b4cfb89f4f06b339c766f92648e798a96747d4163f2fa78660d1ab1f1b5e"},
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||||
{file = "av-12.0.0-cp310-cp310-win_amd64.whl", hash = "sha256:41dcb8c269fa58a56edf3a3c814c32a0c69586827f132b4e395a951b0ce14fad"},
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||||
{file = "av-12.0.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:4fa78fbe0e4469226512380180063116105048c66cb12e18ab4b518466c57e6c"},
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||||
{file = "av-12.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:60a869be1d6af916e65ea461cb93922f5db0698655ed7a7eae7c3ecd4af4debb"},
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{file = "av-12.0.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:df61811cc551c186f0a0e530d97b8b139453534d0f92c1790a923f666522ceda"},
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{file = "av-12.0.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:99cd2fc53091ebfb9a2fa9dd3580267f5bd1c040d0efd99fbc1a162576b271cb"},
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{file = "av-12.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7a6d4f1e261df48932128e6495772faa4cc23f5dd1512eec73daab82ad9f3240"},
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{file = "av-12.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:6aec88e41a498b1e01e2dce5371557e20f9a51aae0c16decc5924ec0be2e22b6"},
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||||
{file = "av-12.0.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:90eb8f2d548e96cbc6f78e89c911cdb15a3d80fd944f31111660ce45939cd037"},
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||||
{file = "av-12.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:d7f3a02910e77d750dbd516256a16db15030e5371530ff5a5ae902dc03d9005d"},
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||||
{file = "av-12.0.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e2477cc51526aa50575313d66e5e8ad7ab944588469be5e557b360ed572ae536"},
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||||
{file = "av-12.0.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a2f47149d3ca6deb79f3e515b8bef50e27ebdb160813e6d67dba77278d2a7883"},
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{file = "av-12.0.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3306e4a3ce8b5bfcc3075793d4ed3a2df69179d8fba22cb944a6164dc235dfb6"},
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||||
{file = "av-12.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:dc1b742e7f6df1b499fb960bd6697d1dd8e7ada7484a041a8c20e70a87225f53"},
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||||
{file = "av-12.0.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:0183be6889e835e1b074b4037bfce4fd44671c606cf1c4ab92ea2f271b544aec"},
|
||||
{file = "av-12.0.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:57337f20b208292ec8d3b11e4d289d8688a43d728174850a81b865d3253fff2c"},
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||||
{file = "av-12.0.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0ec915e8f6521545a38566eefc281042ee504ea3cee0618d8558e4920588b3b2"},
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||||
{file = "av-12.0.0-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:33ad5c0a23c45b72bd6bd47f3b2c1adcd2935ee3d0b6178ed66bba62b964ff31"},
|
||||
{file = "av-12.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bfc3a652b12c93120514d56cf025da47442c5ba51530cdf7ba3660257dbb0de1"},
|
||||
{file = "av-12.0.0-cp38-cp38-win_amd64.whl", hash = "sha256:037f793dd1ef4a1f57f090191a7f803ad10ec82da0d04ea26bbe0b8a145fe927"},
|
||||
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|
||||
{file = "av-12.0.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:abf0c4bc40a0af8a30f4cd96f3be6f19fbce0f21222d7fcec148e085127153f7"},
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||||
{file = "av-12.0.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:81cedd1c072fbebf606724c406b1a1b00adc711f1dfd2bc04c633ce39d8439d8"},
|
||||
{file = "av-12.0.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:02d60f48be9f15dcda37d50f3ce8d7249d9a455643d4322dd3449986bacfc628"},
|
||||
{file = "av-12.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5d2619e4c26d661eecfc404f7d739d8b35f0dcef353fabe61512e030254b7031"},
|
||||
{file = "av-12.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:1892cc91c888d101777d5432d54e0554c11d1c3a2c65d02a2cae0a2256a8fbb9"},
|
||||
{file = "av-12.0.0-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:4819e3ef6c3a44ef6f75907229133a1ee7f688245b2cf49b6b8e969a81ca72c9"},
|
||||
{file = "av-12.0.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bb16bb314cf1503b0250fc46b2c455ee196584231101be0123f4f78638227b62"},
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{file = "av-12.0.0-pp310-pypy310_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f3e6a62bda9a1e144feeb59bbee046d7a2d98399634a30f57e4990197313c158"},
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|
||||
{file = "av-12.0.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:e1d255be317b7c1ebdc4dae98935b9f3869161112dc829c625e54f90d8bdd7ab"},
|
||||
{file = "av-12.0.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:17964b36e08435910aabd5b3f7dca12f99536902529767d276026bc08f94ced7"},
|
||||
{file = "av-12.0.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d2d5f78de29edee06ddcdd4c2b759914575492d6a0cd4de2ce31ee63a4953eff"},
|
||||
{file = "av-12.0.0-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:309b32bc97158d0f0c19e273b8e17a855a86806b7194aebc23bd497326cff11f"},
|
||||
{file = "av-12.0.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c409c71bd9c7c2f8d018c822f36b1447cfa96eca158381a96f3319bb0ff6e79e"},
|
||||
{file = "av-12.0.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:08fc5eaef60a257d622998626e233bf3ff90d2f817f6695d6a27e0ffcfe9dcff"},
|
||||
{file = "av-12.0.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:746ab0eff8a7a21a6c6d16e6b6e61709527eba2ad1a524d92a01bb60d02a3df7"},
|
||||
{file = "av-12.0.0-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:013b3ac3de3aa1c137af0cedafd364fd1c7524ab3e1cd53e04564fd1632ac04d"},
|
||||
{file = "av-12.0.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0fa55923527648f51ac005e44fe2797ebc67f53ad4850e0194d3753761ee33a2"},
|
||||
{file = "av-12.0.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:35d514f4dee0cf67e9e6b2a65fb4a28f98da88e71e8c7f7960bd04625d9fe965"},
|
||||
{file = "av-12.0.0.tar.gz", hash = "sha256:bcf21ebb722d4538b4099e5a78f730d78814dd70003511c185941dba5651b14d"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -604,6 +604,16 @@ files = [
|
|||
[package.dependencies]
|
||||
six = ">=1.4.0"
|
||||
|
||||
[[package]]
|
||||
name = "egl-probe"
|
||||
version = "1.0.2"
|
||||
description = ""
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "egl_probe-1.0.2.tar.gz", hash = "sha256:29bdca7b08da1e060cfb42cd46af8300a7ac4f3b1b2eeb16e545ea16d9a5ac93"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "einops"
|
||||
version = "0.7.0"
|
||||
|
|
@ -763,6 +773,72 @@ files = [
|
|||
[package.extras]
|
||||
preview = ["glfw-preview"]
|
||||
|
||||
[[package]]
|
||||
name = "grpcio"
|
||||
version = "1.62.1"
|
||||
description = "HTTP/2-based RPC framework"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "grpcio-1.62.1-cp310-cp310-linux_armv7l.whl", hash = "sha256:179bee6f5ed7b5f618844f760b6acf7e910988de77a4f75b95bbfaa8106f3c1e"},
|
||||
{file = "grpcio-1.62.1-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:48611e4fa010e823ba2de8fd3f77c1322dd60cb0d180dc6630a7e157b205f7ea"},
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||||
{file = "grpcio-1.62.1-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:b2a0e71b0a2158aa4bce48be9f8f9eb45cbd17c78c7443616d00abbe2a509f6d"},
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{file = "grpcio-1.62.1-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:fbe80577c7880911d3ad65e5ecc997416c98f354efeba2f8d0f9112a67ed65a5"},
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{file = "grpcio-1.62.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:58f6c693d446964e3292425e1d16e21a97a48ba9172f2d0df9d7b640acb99243"},
|
||||
{file = "grpcio-1.62.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:77c339403db5a20ef4fed02e4d1a9a3d9866bf9c0afc77a42234677313ea22f3"},
|
||||
{file = "grpcio-1.62.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:b5a4ea906db7dec694098435d84bf2854fe158eb3cd51e1107e571246d4d1d70"},
|
||||
{file = "grpcio-1.62.1-cp310-cp310-win32.whl", hash = "sha256:4187201a53f8561c015bc745b81a1b2d278967b8de35f3399b84b0695e281d5f"},
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||||
{file = "grpcio-1.62.1-cp310-cp310-win_amd64.whl", hash = "sha256:844d1f3fb11bd1ed362d3fdc495d0770cfab75761836193af166fee113421d66"},
|
||||
{file = "grpcio-1.62.1-cp311-cp311-linux_armv7l.whl", hash = "sha256:833379943d1728a005e44103f17ecd73d058d37d95783eb8f0b28ddc1f54d7b2"},
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||||
{file = "grpcio-1.62.1-cp311-cp311-macosx_10_10_universal2.whl", hash = "sha256:c7fcc6a32e7b7b58f5a7d27530669337a5d587d4066060bcb9dee7a8c833dfb7"},
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{file = "grpcio-1.62.1-cp311-cp311-manylinux_2_17_aarch64.whl", hash = "sha256:fa7d28eb4d50b7cbe75bb8b45ed0da9a1dc5b219a0af59449676a29c2eed9698"},
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{file = "grpcio-1.62.1-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:48f7135c3de2f298b833be8b4ae20cafe37091634e91f61f5a7eb3d61ec6f660"},
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{file = "grpcio-1.62.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:71f11fd63365ade276c9d4a7b7df5c136f9030e3457107e1791b3737a9b9ed6a"},
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||||
{file = "grpcio-1.62.1-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:4b49fd8fe9f9ac23b78437da94c54aa7e9996fbb220bac024a67469ce5d0825f"},
|
||||
{file = "grpcio-1.62.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:482ae2ae78679ba9ed5752099b32e5fe580443b4f798e1b71df412abf43375db"},
|
||||
{file = "grpcio-1.62.1-cp311-cp311-win32.whl", hash = "sha256:1faa02530b6c7426404372515fe5ddf66e199c2ee613f88f025c6f3bd816450c"},
|
||||
{file = "grpcio-1.62.1-cp311-cp311-win_amd64.whl", hash = "sha256:5bd90b8c395f39bc82a5fb32a0173e220e3f401ff697840f4003e15b96d1befc"},
|
||||
{file = "grpcio-1.62.1-cp312-cp312-linux_armv7l.whl", hash = "sha256:b134d5d71b4e0837fff574c00e49176051a1c532d26c052a1e43231f252d813b"},
|
||||
{file = "grpcio-1.62.1-cp312-cp312-macosx_10_10_universal2.whl", hash = "sha256:d1f6c96573dc09d50dbcbd91dbf71d5cf97640c9427c32584010fbbd4c0e0037"},
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||||
{file = "grpcio-1.62.1-cp312-cp312-manylinux_2_17_aarch64.whl", hash = "sha256:359f821d4578f80f41909b9ee9b76fb249a21035a061a327f91c953493782c31"},
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||||
{file = "grpcio-1.62.1-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a485f0c2010c696be269184bdb5ae72781344cb4e60db976c59d84dd6354fac9"},
|
||||
{file = "grpcio-1.62.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b50b09b4dc01767163d67e1532f948264167cd27f49e9377e3556c3cba1268e1"},
|
||||
{file = "grpcio-1.62.1-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:3227c667dccbe38f2c4d943238b887bac588d97c104815aecc62d2fd976e014b"},
|
||||
{file = "grpcio-1.62.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:3952b581eb121324853ce2b191dae08badb75cd493cb4e0243368aa9e61cfd41"},
|
||||
{file = "grpcio-1.62.1-cp312-cp312-win32.whl", hash = "sha256:83a17b303425104d6329c10eb34bba186ffa67161e63fa6cdae7776ff76df73f"},
|
||||
{file = "grpcio-1.62.1-cp312-cp312-win_amd64.whl", hash = "sha256:6696ffe440333a19d8d128e88d440f91fb92c75a80ce4b44d55800e656a3ef1d"},
|
||||
{file = "grpcio-1.62.1-cp37-cp37m-linux_armv7l.whl", hash = "sha256:e3393b0823f938253370ebef033c9fd23d27f3eae8eb9a8f6264900c7ea3fb5a"},
|
||||
{file = "grpcio-1.62.1-cp37-cp37m-macosx_10_10_universal2.whl", hash = "sha256:83e7ccb85a74beaeae2634f10eb858a0ed1a63081172649ff4261f929bacfd22"},
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||||
{file = "grpcio-1.62.1-cp37-cp37m-manylinux_2_17_aarch64.whl", hash = "sha256:882020c87999d54667a284c7ddf065b359bd00251fcd70279ac486776dbf84ec"},
|
||||
{file = "grpcio-1.62.1-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a10383035e864f386fe096fed5c47d27a2bf7173c56a6e26cffaaa5a361addb1"},
|
||||
{file = "grpcio-1.62.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:960edebedc6b9ada1ef58e1c71156f28689978188cd8cff3b646b57288a927d9"},
|
||||
{file = "grpcio-1.62.1-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:23e2e04b83f347d0aadde0c9b616f4726c3d76db04b438fd3904b289a725267f"},
|
||||
{file = "grpcio-1.62.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:978121758711916d34fe57c1f75b79cdfc73952f1481bb9583399331682d36f7"},
|
||||
{file = "grpcio-1.62.1-cp37-cp37m-win_amd64.whl", hash = "sha256:9084086190cc6d628f282e5615f987288b95457292e969b9205e45b442276407"},
|
||||
{file = "grpcio-1.62.1-cp38-cp38-linux_armv7l.whl", hash = "sha256:22bccdd7b23c420a27fd28540fb5dcbc97dc6be105f7698cb0e7d7a420d0e362"},
|
||||
{file = "grpcio-1.62.1-cp38-cp38-macosx_10_10_universal2.whl", hash = "sha256:8999bf1b57172dbc7c3e4bb3c732658e918f5c333b2942243f10d0d653953ba9"},
|
||||
{file = "grpcio-1.62.1-cp38-cp38-manylinux_2_17_aarch64.whl", hash = "sha256:d9e52558b8b8c2f4ac05ac86344a7417ccdd2b460a59616de49eb6933b07a0bd"},
|
||||
{file = "grpcio-1.62.1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1714e7bc935780bc3de1b3fcbc7674209adf5208ff825799d579ffd6cd0bd505"},
|
||||
{file = "grpcio-1.62.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c8842ccbd8c0e253c1f189088228f9b433f7a93b7196b9e5b6f87dba393f5d5d"},
|
||||
{file = "grpcio-1.62.1-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:1f1e7b36bdff50103af95a80923bf1853f6823dd62f2d2a2524b66ed74103e49"},
|
||||
{file = "grpcio-1.62.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:bba97b8e8883a8038606480d6b6772289f4c907f6ba780fa1f7b7da7dfd76f06"},
|
||||
{file = "grpcio-1.62.1-cp38-cp38-win32.whl", hash = "sha256:a7f615270fe534548112a74e790cd9d4f5509d744dd718cd442bf016626c22e4"},
|
||||
{file = "grpcio-1.62.1-cp38-cp38-win_amd64.whl", hash = "sha256:e6c8c8693df718c5ecbc7babb12c69a4e3677fd11de8886f05ab22d4e6b1c43b"},
|
||||
{file = "grpcio-1.62.1-cp39-cp39-linux_armv7l.whl", hash = "sha256:73db2dc1b201d20ab7083e7041946910bb991e7e9761a0394bbc3c2632326483"},
|
||||
{file = "grpcio-1.62.1-cp39-cp39-macosx_10_10_universal2.whl", hash = "sha256:407b26b7f7bbd4f4751dbc9767a1f0716f9fe72d3d7e96bb3ccfc4aace07c8de"},
|
||||
{file = "grpcio-1.62.1-cp39-cp39-manylinux_2_17_aarch64.whl", hash = "sha256:f8de7c8cef9261a2d0a62edf2ccea3d741a523c6b8a6477a340a1f2e417658de"},
|
||||
{file = "grpcio-1.62.1-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9bd5c8a1af40ec305d001c60236308a67e25419003e9bb3ebfab5695a8d0b369"},
|
||||
{file = "grpcio-1.62.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:be0477cb31da67846a33b1a75c611f88bfbcd427fe17701b6317aefceee1b96f"},
|
||||
{file = "grpcio-1.62.1-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:60dcd824df166ba266ee0cfaf35a31406cd16ef602b49f5d4dfb21f014b0dedd"},
|
||||
{file = "grpcio-1.62.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:973c49086cabab773525f6077f95e5a993bfc03ba8fc32e32f2c279497780585"},
|
||||
{file = "grpcio-1.62.1-cp39-cp39-win32.whl", hash = "sha256:12859468e8918d3bd243d213cd6fd6ab07208195dc140763c00dfe901ce1e1b4"},
|
||||
{file = "grpcio-1.62.1-cp39-cp39-win_amd64.whl", hash = "sha256:b7209117bbeebdfa5d898205cc55153a51285757902dd73c47de498ad4d11332"},
|
||||
{file = "grpcio-1.62.1.tar.gz", hash = "sha256:6c455e008fa86d9e9a9d85bb76da4277c0d7d9668a3bfa70dbe86e9f3c759947"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
protobuf = ["grpcio-tools (>=1.62.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "gym"
|
||||
version = "0.26.2"
|
||||
|
|
@ -1038,13 +1114,13 @@ setuptools = "*"
|
|||
|
||||
[[package]]
|
||||
name = "importlib-metadata"
|
||||
version = "7.0.2"
|
||||
version = "7.1.0"
|
||||
description = "Read metadata from Python packages"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "importlib_metadata-7.0.2-py3-none-any.whl", hash = "sha256:f4bc4c0c070c490abf4ce96d715f68e95923320370efb66143df00199bb6c100"},
|
||||
{file = "importlib_metadata-7.0.2.tar.gz", hash = "sha256:198f568f3230878cb1b44fbd7975f87906c22336dba2e4a7f05278c281fbd792"},
|
||||
{file = "importlib_metadata-7.1.0-py3-none-any.whl", hash = "sha256:30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570"},
|
||||
{file = "importlib_metadata-7.1.0.tar.gz", hash = "sha256:b78938b926ee8d5f020fc4772d487045805a55ddbad2ecf21c6d60938dc7fcd2"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1053,7 +1129,7 @@ zipp = ">=0.5"
|
|||
[package.extras]
|
||||
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
|
||||
perf = ["ipython"]
|
||||
testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
|
||||
testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "iniconfig"
|
||||
|
|
@ -1265,6 +1341,21 @@ html5 = ["html5lib"]
|
|||
htmlsoup = ["BeautifulSoup4"]
|
||||
source = ["Cython (>=3.0.7)"]
|
||||
|
||||
[[package]]
|
||||
name = "markdown"
|
||||
version = "3.6"
|
||||
description = "Python implementation of John Gruber's Markdown."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "Markdown-3.6-py3-none-any.whl", hash = "sha256:48f276f4d8cfb8ce6527c8f79e2ee29708508bf4d40aa410fbc3b4ee832c850f"},
|
||||
{file = "Markdown-3.6.tar.gz", hash = "sha256:ed4f41f6daecbeeb96e576ce414c41d2d876daa9a16cb35fa8ed8c2ddfad0224"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"]
|
||||
testing = ["coverage", "pyyaml"]
|
||||
|
||||
[[package]]
|
||||
name = "markupsafe"
|
||||
version = "2.1.5"
|
||||
|
|
@ -2460,6 +2551,30 @@ urllib3 = ">=1.21.1,<3"
|
|||
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
|
||||
use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
|
||||
|
||||
[[package]]
|
||||
name = "robomimic"
|
||||
version = "0.2.0"
|
||||
description = "robomimic: A Modular Framework for Robot Learning from Demonstration"
|
||||
optional = false
|
||||
python-versions = ">=3"
|
||||
files = [
|
||||
{file = "robomimic-0.2.0.tar.gz", hash = "sha256:ee3bb5cf9c3e1feead6b57b43c5db738fd0a8e0c015fdf6419808af8fffdc463"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
egl_probe = ">=1.0.1"
|
||||
h5py = "*"
|
||||
imageio = "*"
|
||||
imageio-ffmpeg = "*"
|
||||
numpy = ">=1.13.3"
|
||||
psutil = "*"
|
||||
tensorboard = "*"
|
||||
tensorboardX = "*"
|
||||
termcolor = "*"
|
||||
torch = "*"
|
||||
torchvision = "*"
|
||||
tqdm = "*"
|
||||
|
||||
[[package]]
|
||||
name = "safetensors"
|
||||
version = "0.4.2"
|
||||
|
|
@ -2684,13 +2799,13 @@ test = ["asv", "gmpy2", "hypothesis", "mpmath", "pooch", "pytest", "pytest-cov",
|
|||
|
||||
[[package]]
|
||||
name = "sentry-sdk"
|
||||
version = "1.42.0"
|
||||
version = "1.43.0"
|
||||
description = "Python client for Sentry (https://sentry.io)"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "sentry-sdk-1.42.0.tar.gz", hash = "sha256:4a8364b8f7edbf47f95f7163e48334c96100d9c098f0ae6606e2e18183c223e6"},
|
||||
{file = "sentry_sdk-1.42.0-py2.py3-none-any.whl", hash = "sha256:a654ee7e497a3f5f6368b36d4f04baeab1fe92b3105f7f6965d6ef0de35a9ba4"},
|
||||
{file = "sentry-sdk-1.43.0.tar.gz", hash = "sha256:41df73af89d22921d8733714fb0fc5586c3461907e06688e6537d01a27e0e0f6"},
|
||||
{file = "sentry_sdk-1.43.0-py2.py3-none-any.whl", hash = "sha256:8d768724839ca18d7b4c7463ef7528c40b7aa2bfbf7fe554d5f9a7c044acfd36"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2704,6 +2819,7 @@ asyncpg = ["asyncpg (>=0.23)"]
|
|||
beam = ["apache-beam (>=2.12)"]
|
||||
bottle = ["bottle (>=0.12.13)"]
|
||||
celery = ["celery (>=3)"]
|
||||
celery-redbeat = ["celery-redbeat (>=2)"]
|
||||
chalice = ["chalice (>=1.16.0)"]
|
||||
clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
|
||||
django = ["django (>=1.8)"]
|
||||
|
|
@ -2948,9 +3064,58 @@ files = [
|
|||
[package.dependencies]
|
||||
mpmath = ">=0.19"
|
||||
|
||||
[[package]]
|
||||
name = "tensorboard"
|
||||
version = "2.16.2"
|
||||
description = "TensorBoard lets you watch Tensors Flow"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "tensorboard-2.16.2-py3-none-any.whl", hash = "sha256:9f2b4e7dad86667615c0e5cd072f1ea8403fc032a299f0072d6f74855775cc45"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
absl-py = ">=0.4"
|
||||
grpcio = ">=1.48.2"
|
||||
markdown = ">=2.6.8"
|
||||
numpy = ">=1.12.0"
|
||||
protobuf = ">=3.19.6,<4.24.0 || >4.24.0"
|
||||
setuptools = ">=41.0.0"
|
||||
six = ">1.9"
|
||||
tensorboard-data-server = ">=0.7.0,<0.8.0"
|
||||
werkzeug = ">=1.0.1"
|
||||
|
||||
[[package]]
|
||||
name = "tensorboard-data-server"
|
||||
version = "0.7.2"
|
||||
description = "Fast data loading for TensorBoard"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb"},
|
||||
{file = "tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60"},
|
||||
{file = "tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:ef687163c24185ae9754ed5650eb5bc4d84ff257aabdc33f0cc6f74d8ba54530"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tensorboardx"
|
||||
version = "2.6.2.2"
|
||||
description = "TensorBoardX lets you watch Tensors Flow without Tensorflow"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "tensorboardX-2.6.2.2-py2.py3-none-any.whl", hash = "sha256:160025acbf759ede23fd3526ae9d9bfbfd8b68eb16c38a010ebe326dc6395db8"},
|
||||
{file = "tensorboardX-2.6.2.2.tar.gz", hash = "sha256:c6476d7cd0d529b0b72f4acadb1269f9ed8b22f441e87a84f2a3b940bb87b666"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = "*"
|
||||
packaging = "*"
|
||||
protobuf = ">=3.20"
|
||||
|
||||
[[package]]
|
||||
name = "tensordict"
|
||||
version = "0.4.0+ca4256e"
|
||||
version = "0.4.0+b4c91e8"
|
||||
description = ""
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
|
|
@ -3289,6 +3454,23 @@ perf = ["orjson"]
|
|||
reports = ["pydantic (>=2.0.0)"]
|
||||
sweeps = ["sweeps (>=0.2.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "werkzeug"
|
||||
version = "3.0.1"
|
||||
description = "The comprehensive WSGI web application library."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "werkzeug-3.0.1-py3-none-any.whl", hash = "sha256:90a285dc0e42ad56b34e696398b8122ee4c681833fb35b8334a095d82c56da10"},
|
||||
{file = "werkzeug-3.0.1.tar.gz", hash = "sha256:507e811ecea72b18a404947aded4b3390e1db8f826b494d76550ef45bb3b1dcc"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
MarkupSafe = ">=2.1.1"
|
||||
|
||||
[package.extras]
|
||||
watchdog = ["watchdog (>=2.3)"]
|
||||
|
||||
[[package]]
|
||||
name = "zarr"
|
||||
version = "2.17.1"
|
||||
|
|
@ -3328,4 +3510,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.10"
|
||||
content-hash = "ee86b84a795e6a3e9c2d79f244a87b55589adbe46d549ac38adf48be27c04cf9"
|
||||
content-hash = "1a45c808e1c48bcbf4319d4cf6876771b7d50f40a5a8968a8b7f3af36192bf34"
|
||||
|
|
|
|||
|
|
@ -51,6 +51,7 @@ torchvision = "^0.17.1"
|
|||
h5py = "^3.10.0"
|
||||
dm-control = "1.0.14"
|
||||
huggingface-hub = {extras = ["hf-transfer"], version = "^0.21.4"}
|
||||
robomimic = "0.2.0"
|
||||
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
|
|
|
|||
Loading…
Reference in New Issue