Merge remote-tracking branch 'origin/user/rcadene/2024_03_31_remove_torchrl' into user/rcadene/2024_03_31_remove_torchrl
This commit is contained in:
Cadene
commit
7c8eb7ff19
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@ -1,4 +1,4 @@
|
|||
# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "absl-py"
|
||||
|
|
@ -517,21 +517,11 @@ files = [
|
|||
{file = "distlib-0.3.8.tar.gz", hash = "sha256:1530ea13e350031b6312d8580ddb6b27a104275a31106523b8f123787f494f64"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "dm"
|
||||
version = "1.3"
|
||||
description = "Dict to Data mapper"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "dm-1.3.tar.gz", hash = "sha256:ce77537bf346b5d8c0dc0b5d679cfc4a946faadcd5315e6c80ef6f3af824130d"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "dm-control"
|
||||
version = "1.0.14"
|
||||
description = "Continuous control environments and MuJoCo Python bindings."
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "dm_control-1.0.14-py3-none-any.whl", hash = "sha256:883c63244a7ebf598700a97564ed19fffd3479ca79efd090aed881609cdb9fc6"},
|
||||
|
|
@ -562,7 +552,7 @@ hdf5 = ["h5py"]
|
|||
name = "dm-env"
|
||||
version = "1.6"
|
||||
description = "A Python interface for Reinforcement Learning environments."
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "dm-env-1.6.tar.gz", hash = "sha256:a436eb1c654c39e0c986a516cee218bea7140b510fceff63f97eb4fcff3d93de"},
|
||||
|
|
@ -578,7 +568,7 @@ numpy = "*"
|
|||
name = "dm-tree"
|
||||
version = "0.1.8"
|
||||
description = "Tree is a library for working with nested data structures."
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "dm-tree-0.1.8.tar.gz", hash = "sha256:0fcaabbb14e7980377439e7140bd05552739ca5e515ecb3119f234acee4b9430"},
|
||||
|
|
@ -806,7 +796,7 @@ test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre
|
|||
name = "glfw"
|
||||
version = "2.7.0"
|
||||
description = "A ctypes-based wrapper for GLFW3."
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "glfw-2.7.0-py2.py27.py3.py30.py31.py32.py33.py34.py35.py36.py37.py38-none-macosx_10_6_intel.whl", hash = "sha256:bd82849edcceda4e262bd1227afaa74b94f9f0731c1197863cd25c15bfc613fc"},
|
||||
|
|
@ -986,7 +976,7 @@ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
|
|||
name = "gymnasium-robotics"
|
||||
version = "1.2.4"
|
||||
description = "Robotics environments for the Gymnasium repo."
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "gymnasium-robotics-1.2.4.tar.gz", hash = "sha256:d304192b066f8b800599dfbe3d9d90bba9b761ee884472bdc4d05968a8bc61cb"},
|
||||
|
|
@ -1218,7 +1208,7 @@ i18n = ["Babel (>=2.7)"]
|
|||
name = "labmaze"
|
||||
version = "1.0.6"
|
||||
description = "LabMaze: DeepMind Lab's text maze generator."
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "labmaze-1.0.6-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:b2ddef976dfd8d992b19cfa6c633f2eba7576d759c2082da534e3f727479a84a"},
|
||||
|
|
@ -1262,7 +1252,7 @@ setuptools = "!=50.0.0"
|
|||
name = "lazy-loader"
|
||||
version = "0.3"
|
||||
description = "lazy_loader"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "lazy_loader-0.3-py3-none-any.whl", hash = "sha256:1e9e76ee8631e264c62ce10006718e80b2cfc74340d17d1031e0f84af7478554"},
|
||||
|
|
@ -1307,7 +1297,7 @@ files = [
|
|||
name = "lxml"
|
||||
version = "5.1.0"
|
||||
description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "lxml-5.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:704f5572ff473a5f897745abebc6df40f22d4133c1e0a1f124e4f2bd3330ff7e"},
|
||||
|
|
@ -1525,7 +1515,7 @@ tests = ["pytest (>=4.6)"]
|
|||
name = "mujoco"
|
||||
version = "2.3.7"
|
||||
description = "MuJoCo Physics Simulator"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "mujoco-2.3.7-cp310-cp310-macosx_10_16_x86_64.whl", hash = "sha256:e8714a5ff6a1561b364b7b4648d4c0c8d13e751874cf7401c309b9d23fa9598b"},
|
||||
|
|
@ -1839,7 +1829,7 @@ xml = ["lxml (>=4.9.2)"]
|
|||
name = "pettingzoo"
|
||||
version = "1.24.3"
|
||||
description = "Gymnasium for multi-agent reinforcement learning."
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pettingzoo-1.24.3-py3-none-any.whl", hash = "sha256:23ed90517d2e8a7098bdaf5e31234b3a7f7b73ca578d70d1ca7b9d0cb0e37982"},
|
||||
|
|
@ -2207,7 +2197,7 @@ dev = ["aafigure", "matplotlib", "pygame", "pyglet (<2.0.0)", "sphinx", "wheel"]
|
|||
name = "pyopengl"
|
||||
version = "3.1.7"
|
||||
description = "Standard OpenGL bindings for Python"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "PyOpenGL-3.1.7-py3-none-any.whl", hash = "sha256:a6ab19cf290df6101aaf7470843a9c46207789855746399d0af92521a0a92b7a"},
|
||||
|
|
@ -2218,7 +2208,7 @@ files = [
|
|||
name = "pyparsing"
|
||||
version = "3.1.2"
|
||||
description = "pyparsing module - Classes and methods to define and execute parsing grammars"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.6.8"
|
||||
files = [
|
||||
{file = "pyparsing-3.1.2-py3-none-any.whl", hash = "sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742"},
|
||||
|
|
@ -2649,7 +2639,7 @@ torch = ["safetensors[numpy]", "torch (>=1.10)"]
|
|||
name = "scikit-image"
|
||||
version = "0.22.0"
|
||||
description = "Image processing in Python"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "scikit_image-0.22.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:74ec5c1d4693506842cc7c9487c89d8fc32aed064e9363def7af08b8f8cbb31d"},
|
||||
|
|
@ -2697,7 +2687,7 @@ test = ["asv", "matplotlib (>=3.5)", "numpydoc (>=1.5)", "pooch (>=1.6.0)", "pyt
|
|||
name = "scipy"
|
||||
version = "1.12.0"
|
||||
description = "Fundamental algorithms for scientific computing in Python"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "scipy-1.12.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:78e4402e140879387187f7f25d91cc592b3501a2e51dfb320f48dfb73565f10b"},
|
||||
|
|
@ -2902,7 +2892,7 @@ testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jar
|
|||
name = "shapely"
|
||||
version = "2.0.3"
|
||||
description = "Manipulation and analysis of geometric objects"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "shapely-2.0.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:af7e9abe180b189431b0f490638281b43b84a33a960620e6b2e8d3e3458b61a1"},
|
||||
|
|
@ -3069,7 +3059,7 @@ tests = ["pytest", "pytest-cov"]
|
|||
name = "tifffile"
|
||||
version = "2024.2.12"
|
||||
description = "Read and write TIFF files"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "tifffile-2024.2.12-py3-none-any.whl", hash = "sha256:870998f82fbc94ff7c3528884c1b0ae54863504ff51dbebea431ac3fa8fb7c21"},
|
||||
|
|
@ -3331,7 +3321,12 @@ files = [
|
|||
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
|
||||
testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
|
||||
|
||||
[extras]
|
||||
aloha = ["gym-aloha"]
|
||||
pusht = ["gym-pusht"]
|
||||
xarm = ["gym-xarm"]
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.10"
|
||||
content-hash = "32cd6caa01276a90b37cb177204e5b1511e92838f3f0268391034042d56f3bd6"
|
||||
content-hash = "8fa6dfc30e605741c24f5de58b89125d5b02153f550e5af7a44356956d6bb167"
|
||||
|
|
|
|||
|
|
@ -23,7 +23,6 @@ packages = [{include = "lerobot"}]
|
|||
python = "^3.10"
|
||||
termcolor = "^2.4.0"
|
||||
omegaconf = "^2.3.0"
|
||||
dm-env = "^1.6"
|
||||
pandas = "^2.2.1"
|
||||
wandb = "^0.16.3"
|
||||
moviepy = "^1.0.3"
|
||||
|
|
@ -34,21 +33,15 @@ einops = "^0.7.0"
|
|||
pygame = "^2.5.2"
|
||||
pymunk = "^6.6.0"
|
||||
zarr = "^2.17.0"
|
||||
shapely = "^2.0.3"
|
||||
scikit-image = "^0.22.0"
|
||||
numba = "^0.59.0"
|
||||
mpmath = "^1.3.0"
|
||||
torch = {version = "^2.2.1", source = "torch-cpu"}
|
||||
mujoco = "^2.3.7"
|
||||
opencv-python = "^4.9.0.80"
|
||||
diffusers = "^0.26.3"
|
||||
torchvision = {version = "^0.17.1", source = "torch-cpu"}
|
||||
h5py = "^3.10.0"
|
||||
dm = "^1.3"
|
||||
dm-control = "1.0.14"
|
||||
robomimic = "0.2.0"
|
||||
huggingface-hub = "^0.21.4"
|
||||
gymnasium-robotics = "^1.2.4"
|
||||
gymnasium = "^0.29.1"
|
||||
cmake = "^3.29.0.1"
|
||||
gym-pusht = { git = "git@github.com:huggingface/gym-pusht.git", optional = true}
|
||||
|
|
@ -58,9 +51,23 @@ gym-aloha = { git = "git@github.com:huggingface/gym-aloha.git", optional = true}
|
|||
# gym-xarm = { path = "../gym-xarm", develop = true, optional = true}
|
||||
# gym-aloha = { path = "../gym-aloha", develop = true, optional = true}
|
||||
|
||||
|
||||
[tool.poetry.extras]
|
||||
pusht = ["gym-pusht"]
|
||||
xarm = ["gym-xarm"]
|
||||
aloha = ["gym-aloha"]
|
||||
|
||||
|
||||
[tool.poetry.group.dev]
|
||||
optional = true
|
||||
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
pre-commit = "^3.6.2"
|
||||
debugpy = "^1.8.1"
|
||||
|
||||
|
||||
[tool.poetry.group.test.dependencies]
|
||||
pytest = "^8.1.0"
|
||||
pytest-cov = "^5.0.0"
|
||||
|
||||
|
|
|
|||
|
|
@ -34,6 +34,11 @@ jobs:
|
|||
with:
|
||||
python-version: '3.10'
|
||||
|
||||
- name: Add SSH key for installing envs
|
||||
uses: webfactory/ssh-agent@v0.9.0
|
||||
with:
|
||||
ssh-private-key: ${{ secrets.SSH_PRIVATE_KEY }}
|
||||
|
||||
#----------------------------------------------
|
||||
# install & configure poetry
|
||||
#----------------------------------------------
|
||||
|
|
@ -87,7 +92,7 @@ jobs:
|
|||
TMP: ~/tmp
|
||||
run: |
|
||||
mkdir ~/tmp
|
||||
poetry install --no-interaction --no-root
|
||||
poetry install --no-interaction --no-root --all-extras
|
||||
|
||||
- name: Save cached venv
|
||||
if: |
|
||||
|
|
@ -106,7 +111,7 @@ jobs:
|
|||
# install project
|
||||
#----------------------------------------------
|
||||
- name: Install project
|
||||
run: poetry install --no-interaction
|
||||
run: poetry install --no-interaction --all-extras
|
||||
|
||||
#----------------------------------------------
|
||||
# run tests & coverage
|
||||
|
|
|
|||
|
|
@ -158,8 +158,7 @@ class AlohaDataset(torch.utils.data.Dataset):
|
|||
self.data_ids_per_episode = {}
|
||||
ep_dicts = []
|
||||
|
||||
idx0 = idx1 = 0
|
||||
logging.info("Initialize and feed offline buffer")
|
||||
frame_idx = 0
|
||||
for ep_id in tqdm.tqdm(range(NUM_EPISODES[self.dataset_id])):
|
||||
ep_path = raw_dir / f"episode_{ep_id}.hdf5"
|
||||
with h5py.File(ep_path, "r") as ep:
|
||||
|
|
@ -191,15 +190,13 @@ class AlohaDataset(torch.utils.data.Dataset):
|
|||
ep_dict[f"observation.images.{cam}"] = image[:-1]
|
||||
# ep_dict[f"next.observation.images.{cam}"] = image[1:]
|
||||
|
||||
assert isinstance(ep_id, int)
|
||||
self.data_ids_per_episode[ep_id] = torch.arange(frame_idx, frame_idx + num_frames, 1)
|
||||
assert len(self.data_ids_per_episode[ep_id]) == num_frames
|
||||
|
||||
ep_dicts.append(ep_dict)
|
||||
|
||||
idx1 += num_frames
|
||||
|
||||
assert isinstance(ep_id, int)
|
||||
self.data_ids_per_episode[ep_id] = torch.arange(idx0, idx1, 1)
|
||||
assert len(self.data_ids_per_episode[ep_id]) == num_frames
|
||||
|
||||
idx0 = idx1
|
||||
frame_idx += num_frames
|
||||
|
||||
self.data_dict = {}
|
||||
|
||||
|
|
|
|||
|
|
@ -30,10 +30,13 @@ def make_env(cfg, num_parallel_envs=0) -> gym.Env | gym.vector.SyncVectorEnv:
|
|||
|
||||
if num_parallel_envs == 0:
|
||||
# non-batched version of the env that returns an observation of shape (c)
|
||||
env = gym.make(gym_handle, **kwargs)
|
||||
env = gym.make(gym_handle, disable_env_checker=True, **kwargs)
|
||||
else:
|
||||
# batched version of the env that returns an observation of shape (b, c)
|
||||
env = gym.vector.SyncVectorEnv(
|
||||
[lambda: gym.make(gym_handle, **kwargs) for _ in range(num_parallel_envs)]
|
||||
[
|
||||
lambda: gym.make(gym_handle, disable_env_checker=True, **kwargs)
|
||||
for _ in range(num_parallel_envs)
|
||||
]
|
||||
)
|
||||
return env
|
||||
|
|
|
|||
|
|
@ -1,115 +0,0 @@
|
|||
from typing import List
|
||||
|
||||
import torch
|
||||
import torchvision
|
||||
from torch import nn
|
||||
from torchvision.models._utils import IntermediateLayerGetter
|
||||
|
||||
from .position_encoding import build_position_encoding
|
||||
from .utils import NestedTensor, is_main_process
|
||||
|
||||
|
||||
class FrozenBatchNorm2d(torch.nn.Module):
|
||||
"""
|
||||
BatchNorm2d where the batch statistics and the affine parameters are fixed.
|
||||
|
||||
Copy-paste from torchvision.misc.ops with added eps before rqsrt,
|
||||
without which any other policy_models than torchvision.policy_models.resnet[18,34,50,101]
|
||||
produce nans.
|
||||
"""
|
||||
|
||||
def __init__(self, n):
|
||||
super().__init__()
|
||||
self.register_buffer("weight", torch.ones(n))
|
||||
self.register_buffer("bias", torch.zeros(n))
|
||||
self.register_buffer("running_mean", torch.zeros(n))
|
||||
self.register_buffer("running_var", torch.ones(n))
|
||||
|
||||
def _load_from_state_dict(
|
||||
self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
|
||||
):
|
||||
num_batches_tracked_key = prefix + "num_batches_tracked"
|
||||
if num_batches_tracked_key in state_dict:
|
||||
del state_dict[num_batches_tracked_key]
|
||||
|
||||
super()._load_from_state_dict(
|
||||
state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
# move reshapes to the beginning
|
||||
# to make it fuser-friendly
|
||||
w = self.weight.reshape(1, -1, 1, 1)
|
||||
b = self.bias.reshape(1, -1, 1, 1)
|
||||
rv = self.running_var.reshape(1, -1, 1, 1)
|
||||
rm = self.running_mean.reshape(1, -1, 1, 1)
|
||||
eps = 1e-5
|
||||
scale = w * (rv + eps).rsqrt()
|
||||
bias = b - rm * scale
|
||||
return x * scale + bias
|
||||
|
||||
|
||||
class BackboneBase(nn.Module):
|
||||
def __init__(
|
||||
self, backbone: nn.Module, train_backbone: bool, num_channels: int, return_interm_layers: bool
|
||||
):
|
||||
super().__init__()
|
||||
# for name, parameter in backbone.named_parameters(): # only train later layers # TODO do we want this?
|
||||
# if not train_backbone or 'layer2' not in name and 'layer3' not in name and 'layer4' not in name:
|
||||
# parameter.requires_grad_(False)
|
||||
if return_interm_layers:
|
||||
return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"}
|
||||
else:
|
||||
return_layers = {"layer4": "0"}
|
||||
self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
|
||||
self.num_channels = num_channels
|
||||
|
||||
def forward(self, tensor):
|
||||
xs = self.body(tensor)
|
||||
return xs
|
||||
# out: Dict[str, NestedTensor] = {}
|
||||
# for name, x in xs.items():
|
||||
# m = tensor_list.mask
|
||||
# assert m is not None
|
||||
# mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
|
||||
# out[name] = NestedTensor(x, mask)
|
||||
# return out
|
||||
|
||||
|
||||
class Backbone(BackboneBase):
|
||||
"""ResNet backbone with frozen BatchNorm."""
|
||||
|
||||
def __init__(self, name: str, train_backbone: bool, return_interm_layers: bool, dilation: bool):
|
||||
backbone = getattr(torchvision.models, name)(
|
||||
replace_stride_with_dilation=[False, False, dilation],
|
||||
pretrained=is_main_process(),
|
||||
norm_layer=FrozenBatchNorm2d,
|
||||
) # pretrained # TODO do we want frozen batch_norm??
|
||||
num_channels = 512 if name in ("resnet18", "resnet34") else 2048
|
||||
super().__init__(backbone, train_backbone, num_channels, return_interm_layers)
|
||||
|
||||
|
||||
class Joiner(nn.Sequential):
|
||||
def __init__(self, backbone, position_embedding):
|
||||
super().__init__(backbone, position_embedding)
|
||||
|
||||
def forward(self, tensor_list: NestedTensor):
|
||||
xs = self[0](tensor_list)
|
||||
out: List[NestedTensor] = []
|
||||
pos = []
|
||||
for _, x in xs.items():
|
||||
out.append(x)
|
||||
# position encoding
|
||||
pos.append(self[1](x).to(x.dtype))
|
||||
|
||||
return out, pos
|
||||
|
||||
|
||||
def build_backbone(args):
|
||||
position_embedding = build_position_encoding(args)
|
||||
train_backbone = args.lr_backbone > 0
|
||||
return_interm_layers = args.masks
|
||||
backbone = Backbone(args.backbone, train_backbone, return_interm_layers, args.dilation)
|
||||
model = Joiner(backbone, position_embedding)
|
||||
model.num_channels = backbone.num_channels
|
||||
return model
|
||||
|
|
@ -1,212 +0,0 @@
|
|||
import numpy as np
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.autograd import Variable
|
||||
|
||||
from .backbone import build_backbone
|
||||
from .transformer import TransformerEncoder, TransformerEncoderLayer, build_transformer
|
||||
|
||||
|
||||
def reparametrize(mu, logvar):
|
||||
std = logvar.div(2).exp()
|
||||
eps = Variable(std.data.new(std.size()).normal_())
|
||||
return mu + std * eps
|
||||
|
||||
|
||||
def get_sinusoid_encoding_table(n_position, d_hid):
|
||||
def get_position_angle_vec(position):
|
||||
return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)]
|
||||
|
||||
sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)])
|
||||
sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i
|
||||
sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1
|
||||
|
||||
return torch.FloatTensor(sinusoid_table).unsqueeze(0)
|
||||
|
||||
|
||||
class DETRVAE(nn.Module):
|
||||
"""This is the DETR module that performs object detection"""
|
||||
|
||||
def __init__(
|
||||
self, backbones, transformer, encoder, state_dim, action_dim, num_queries, camera_names, vae
|
||||
):
|
||||
"""Initializes the model.
|
||||
Parameters:
|
||||
backbones: torch module of the backbone to be used. See backbone.py
|
||||
transformer: torch module of the transformer architecture. See transformer.py
|
||||
state_dim: robot state dimension of the environment
|
||||
num_queries: number of object queries, ie detection slot. This is the maximal number of objects
|
||||
DETR can detect in a single image. For COCO, we recommend 100 queries.
|
||||
aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
|
||||
"""
|
||||
super().__init__()
|
||||
self.num_queries = num_queries
|
||||
self.camera_names = camera_names
|
||||
self.transformer = transformer
|
||||
self.encoder = encoder
|
||||
self.vae = vae
|
||||
hidden_dim = transformer.d_model
|
||||
self.action_head = nn.Linear(hidden_dim, action_dim)
|
||||
self.is_pad_head = nn.Linear(hidden_dim, 1)
|
||||
self.query_embed = nn.Embedding(num_queries, hidden_dim)
|
||||
if backbones is not None:
|
||||
self.input_proj = nn.Conv2d(backbones[0].num_channels, hidden_dim, kernel_size=1)
|
||||
self.backbones = nn.ModuleList(backbones)
|
||||
self.input_proj_robot_state = nn.Linear(state_dim, hidden_dim)
|
||||
else:
|
||||
# input_dim = 14 + 7 # robot_state + env_state
|
||||
self.input_proj_robot_state = nn.Linear(state_dim, hidden_dim)
|
||||
# TODO(rcadene): understand what is env_state, and why it needs to be 7
|
||||
self.input_proj_env_state = nn.Linear(state_dim // 2, hidden_dim)
|
||||
self.pos = torch.nn.Embedding(2, hidden_dim)
|
||||
self.backbones = None
|
||||
|
||||
# encoder extra parameters
|
||||
self.latent_dim = 32 # final size of latent z # TODO tune
|
||||
self.cls_embed = nn.Embedding(1, hidden_dim) # extra cls token embedding
|
||||
self.encoder_action_proj = nn.Linear(14, hidden_dim) # project action to embedding
|
||||
self.encoder_joint_proj = nn.Linear(14, hidden_dim) # project qpos to embedding
|
||||
self.latent_proj = nn.Linear(
|
||||
hidden_dim, self.latent_dim * 2
|
||||
) # project hidden state to latent std, var
|
||||
self.register_buffer(
|
||||
"pos_table", get_sinusoid_encoding_table(1 + 1 + num_queries, hidden_dim)
|
||||
) # [CLS], qpos, a_seq
|
||||
|
||||
# decoder extra parameters
|
||||
self.latent_out_proj = nn.Linear(self.latent_dim, hidden_dim) # project latent sample to embedding
|
||||
self.additional_pos_embed = nn.Embedding(
|
||||
2, hidden_dim
|
||||
) # learned position embedding for proprio and latent
|
||||
|
||||
def forward(self, qpos, image, env_state, actions=None, is_pad=None):
|
||||
"""
|
||||
qpos: batch, qpos_dim
|
||||
image: batch, num_cam, channel, height, width
|
||||
env_state: None
|
||||
actions: batch, seq, action_dim
|
||||
"""
|
||||
is_training = actions is not None # train or val
|
||||
bs, _ = qpos.shape
|
||||
### Obtain latent z from action sequence
|
||||
if self.vae and is_training:
|
||||
# project action sequence to embedding dim, and concat with a CLS token
|
||||
action_embed = self.encoder_action_proj(actions) # (bs, seq, hidden_dim)
|
||||
qpos_embed = self.encoder_joint_proj(qpos) # (bs, hidden_dim)
|
||||
qpos_embed = torch.unsqueeze(qpos_embed, axis=1) # (bs, 1, hidden_dim)
|
||||
cls_embed = self.cls_embed.weight # (1, hidden_dim)
|
||||
cls_embed = torch.unsqueeze(cls_embed, axis=0).repeat(bs, 1, 1) # (bs, 1, hidden_dim)
|
||||
encoder_input = torch.cat(
|
||||
[cls_embed, qpos_embed, action_embed], axis=1
|
||||
) # (bs, seq+1, hidden_dim)
|
||||
encoder_input = encoder_input.permute(1, 0, 2) # (seq+1, bs, hidden_dim)
|
||||
# do not mask cls token
|
||||
# cls_joint_is_pad = torch.full((bs, 2), False).to(qpos.device) # False: not a padding
|
||||
# is_pad = torch.cat([cls_joint_is_pad, is_pad], axis=1) # (bs, seq+1)
|
||||
# obtain position embedding
|
||||
pos_embed = self.pos_table.clone().detach()
|
||||
pos_embed = pos_embed.permute(1, 0, 2) # (seq+1, 1, hidden_dim)
|
||||
# query model
|
||||
encoder_output = self.encoder(encoder_input, pos=pos_embed) # , src_key_padding_mask=is_pad)
|
||||
encoder_output = encoder_output[0] # take cls output only
|
||||
latent_info = self.latent_proj(encoder_output)
|
||||
mu = latent_info[:, : self.latent_dim]
|
||||
logvar = latent_info[:, self.latent_dim :]
|
||||
latent_sample = reparametrize(mu, logvar)
|
||||
latent_input = self.latent_out_proj(latent_sample)
|
||||
else:
|
||||
mu = logvar = None
|
||||
latent_sample = torch.zeros([bs, self.latent_dim], dtype=torch.float32).to(qpos.device)
|
||||
latent_input = self.latent_out_proj(latent_sample)
|
||||
|
||||
if self.backbones is not None:
|
||||
# Image observation features and position embeddings
|
||||
all_cam_features = []
|
||||
all_cam_pos = []
|
||||
for cam_id, _ in enumerate(self.camera_names):
|
||||
features, pos = self.backbones[0](image[:, cam_id]) # HARDCODED
|
||||
features = features[0] # take the last layer feature
|
||||
pos = pos[0]
|
||||
all_cam_features.append(self.input_proj(features))
|
||||
all_cam_pos.append(pos)
|
||||
# proprioception features
|
||||
proprio_input = self.input_proj_robot_state(qpos)
|
||||
# fold camera dimension into width dimension
|
||||
src = torch.cat(all_cam_features, axis=3)
|
||||
pos = torch.cat(all_cam_pos, axis=3)
|
||||
hs = self.transformer(
|
||||
src,
|
||||
None,
|
||||
self.query_embed.weight,
|
||||
pos,
|
||||
latent_input,
|
||||
proprio_input,
|
||||
self.additional_pos_embed.weight,
|
||||
)[0]
|
||||
else:
|
||||
qpos = self.input_proj_robot_state(qpos)
|
||||
env_state = self.input_proj_env_state(env_state)
|
||||
transformer_input = torch.cat([qpos, env_state], axis=1) # seq length = 2
|
||||
hs = self.transformer(transformer_input, None, self.query_embed.weight, self.pos.weight)[0]
|
||||
a_hat = self.action_head(hs)
|
||||
is_pad_hat = self.is_pad_head(hs)
|
||||
return a_hat, is_pad_hat, [mu, logvar]
|
||||
|
||||
|
||||
def mlp(input_dim, hidden_dim, output_dim, hidden_depth):
|
||||
if hidden_depth == 0:
|
||||
mods = [nn.Linear(input_dim, output_dim)]
|
||||
else:
|
||||
mods = [nn.Linear(input_dim, hidden_dim), nn.ReLU(inplace=True)]
|
||||
for _ in range(hidden_depth - 1):
|
||||
mods += [nn.Linear(hidden_dim, hidden_dim), nn.ReLU(inplace=True)]
|
||||
mods.append(nn.Linear(hidden_dim, output_dim))
|
||||
trunk = nn.Sequential(*mods)
|
||||
return trunk
|
||||
|
||||
|
||||
def build_encoder(args):
|
||||
d_model = args.hidden_dim # 256
|
||||
dropout = args.dropout # 0.1
|
||||
nhead = args.nheads # 8
|
||||
dim_feedforward = args.dim_feedforward # 2048
|
||||
num_encoder_layers = args.enc_layers # 4 # TODO shared with VAE decoder
|
||||
normalize_before = args.pre_norm # False
|
||||
activation = "relu"
|
||||
|
||||
encoder_layer = TransformerEncoderLayer(
|
||||
d_model, nhead, dim_feedforward, dropout, activation, normalize_before
|
||||
)
|
||||
encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
|
||||
encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
|
||||
|
||||
return encoder
|
||||
|
||||
|
||||
def build(args):
|
||||
# From state
|
||||
# backbone = None # from state for now, no need for conv nets
|
||||
# From image
|
||||
backbones = []
|
||||
backbone = build_backbone(args)
|
||||
backbones.append(backbone)
|
||||
|
||||
transformer = build_transformer(args)
|
||||
|
||||
encoder = build_encoder(args)
|
||||
|
||||
model = DETRVAE(
|
||||
backbones,
|
||||
transformer,
|
||||
encoder,
|
||||
state_dim=args.state_dim,
|
||||
action_dim=args.action_dim,
|
||||
num_queries=args.num_queries,
|
||||
camera_names=args.camera_names,
|
||||
vae=args.vae,
|
||||
)
|
||||
|
||||
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
|
||||
print("number of parameters: {:.2f}M".format(n_parameters / 1e6))
|
||||
|
||||
return model
|
||||
|
|
@ -1,125 +1,419 @@
|
|||
import logging
|
||||
"""Action Chunking Transformer Policy
|
||||
|
||||
As per Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware (https://arxiv.org/abs/2304.13705).
|
||||
The majority of changes here involve removing unused code, unifying naming, and adding helpful comments.
|
||||
"""
|
||||
|
||||
import math
|
||||
import time
|
||||
from collections import deque
|
||||
from itertools import chain
|
||||
from typing import Callable
|
||||
|
||||
import einops
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F # noqa: N812
|
||||
import torchvision
|
||||
import torchvision.transforms as transforms
|
||||
from torch import nn
|
||||
from torch import Tensor, nn
|
||||
from torchvision.models._utils import IntermediateLayerGetter
|
||||
from torchvision.ops.misc import FrozenBatchNorm2d
|
||||
|
||||
from lerobot.common.policies.act.detr_vae import build
|
||||
from lerobot.common.policies.utils import populate_queues
|
||||
|
||||
|
||||
def build_act_model_and_optimizer(cfg):
|
||||
model = build(cfg)
|
||||
|
||||
param_dicts = [
|
||||
{"params": [p for n, p in model.named_parameters() if "backbone" not in n and p.requires_grad]},
|
||||
{
|
||||
"params": [p for n, p in model.named_parameters() if "backbone" in n and p.requires_grad],
|
||||
"lr": cfg.lr_backbone,
|
||||
},
|
||||
]
|
||||
optimizer = torch.optim.AdamW(param_dicts, lr=cfg.lr, weight_decay=cfg.weight_decay)
|
||||
|
||||
return model, optimizer
|
||||
|
||||
|
||||
def kl_divergence(mu, logvar):
|
||||
batch_size = mu.size(0)
|
||||
assert batch_size != 0
|
||||
if mu.data.ndimension() == 4:
|
||||
mu = mu.view(mu.size(0), mu.size(1))
|
||||
if logvar.data.ndimension() == 4:
|
||||
logvar = logvar.view(logvar.size(0), logvar.size(1))
|
||||
|
||||
klds = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp())
|
||||
total_kld = klds.sum(1).mean(0, True)
|
||||
dimension_wise_kld = klds.mean(0)
|
||||
mean_kld = klds.mean(1).mean(0, True)
|
||||
|
||||
return total_kld, dimension_wise_kld, mean_kld
|
||||
from lerobot.common.utils import get_safe_torch_device
|
||||
|
||||
|
||||
class ActionChunkingTransformerPolicy(nn.Module):
|
||||
name = "act"
|
||||
"""
|
||||
Action Chunking Transformer Policy as per Learning Fine-Grained Bimanual Manipulation with Low-Cost
|
||||
Hardware (paper: https://arxiv.org/abs/2304.13705, code: https://github.com/tonyzhaozh/act)
|
||||
|
||||
def __init__(self, cfg, n_obs_steps, n_action_steps):
|
||||
Note: In this code we use the terms `vae_encoder`, 'encoder', `decoder`. The meanings are as follows.
|
||||
- The `vae_encoder` is, as per the literature around variational auto-encoders (VAE), the part of the
|
||||
model that encodes the target data (a sequence of actions), and the condition (the robot
|
||||
joint-space).
|
||||
- A transformer with an `encoder` (not the VAE encoder) and `decoder` (not the VAE decoder) with
|
||||
cross-attention is used as the VAE decoder. For these terms, we drop the `vae_` prefix because we
|
||||
have an option to train this model without the variational objective (in which case we drop the
|
||||
`vae_encoder` altogether, and nothing about this model has anything to do with a VAE).
|
||||
|
||||
Transformer
|
||||
Used alone for inference
|
||||
(acts as VAE decoder
|
||||
during training)
|
||||
┌───────────────────────┐
|
||||
│ Outputs │
|
||||
│ ▲ │
|
||||
│ ┌─────►┌───────┐ │
|
||||
┌──────┐ │ │ │Transf.│ │
|
||||
│ │ │ ├─────►│decoder│ │
|
||||
┌────┴────┐ │ │ │ │ │ │
|
||||
│ │ │ │ ┌───┴───┬─►│ │ │
|
||||
│ VAE │ │ │ │ │ └───────┘ │
|
||||
│ encoder │ │ │ │Transf.│ │
|
||||
│ │ │ │ │encoder│ │
|
||||
└───▲─────┘ │ │ │ │ │
|
||||
│ │ │ └───▲───┘ │
|
||||
│ │ │ │ │
|
||||
inputs └─────┼─────┘ │
|
||||
│ │
|
||||
└───────────────────────┘
|
||||
"""
|
||||
|
||||
name = "act"
|
||||
_multiple_obs_steps_not_handled_msg = (
|
||||
"ActionChunkingTransformerPolicy does not handle multiple observation steps."
|
||||
)
|
||||
|
||||
def __init__(self, cfg, device):
|
||||
"""
|
||||
TODO(alexander-soare): Add documentation for all parameters once we have model configs established.
|
||||
"""
|
||||
super().__init__()
|
||||
if getattr(cfg, "n_obs_steps", 1) != 1:
|
||||
raise ValueError(self._multiple_obs_steps_not_handled_msg)
|
||||
self.cfg = cfg
|
||||
self.n_obs_steps = n_obs_steps
|
||||
if self.n_obs_steps > 1:
|
||||
raise NotImplementedError()
|
||||
self.n_action_steps = n_action_steps
|
||||
self.model, self.optimizer = build_act_model_and_optimizer(cfg)
|
||||
self.kl_weight = self.cfg.kl_weight
|
||||
logging.info(f"KL Weight {self.kl_weight}")
|
||||
self.n_action_steps = cfg.n_action_steps
|
||||
self.device = get_safe_torch_device(device)
|
||||
self.camera_names = cfg.camera_names
|
||||
self.use_vae = cfg.use_vae
|
||||
self.horizon = cfg.horizon
|
||||
self.d_model = cfg.d_model
|
||||
|
||||
transformer_common_kwargs = dict( # noqa: C408
|
||||
d_model=self.d_model,
|
||||
num_heads=cfg.num_heads,
|
||||
dim_feedforward=cfg.dim_feedforward,
|
||||
dropout=cfg.dropout,
|
||||
activation=cfg.activation,
|
||||
normalize_before=cfg.pre_norm,
|
||||
)
|
||||
|
||||
# BERT style VAE encoder with input [cls, *joint_space_configuration, *action_sequence].
|
||||
# The cls token forms parameters of the latent's distribution (like this [*means, *log_variances]).
|
||||
if self.use_vae:
|
||||
self.vae_encoder = _TransformerEncoder(num_layers=cfg.vae_enc_layers, **transformer_common_kwargs)
|
||||
self.vae_encoder_cls_embed = nn.Embedding(1, self.d_model)
|
||||
# Projection layer for joint-space configuration to hidden dimension.
|
||||
self.vae_encoder_robot_state_input_proj = nn.Linear(cfg.state_dim, self.d_model)
|
||||
# Projection layer for action (joint-space target) to hidden dimension.
|
||||
self.vae_encoder_action_input_proj = nn.Linear(cfg.state_dim, self.d_model)
|
||||
self.latent_dim = cfg.latent_dim
|
||||
# Projection layer from the VAE encoder's output to the latent distribution's parameter space.
|
||||
self.vae_encoder_latent_output_proj = nn.Linear(self.d_model, self.latent_dim * 2)
|
||||
# Fixed sinusoidal positional embedding the whole input to the VAE encoder. Unsqueeze for batch
|
||||
# dimension.
|
||||
self.register_buffer(
|
||||
"vae_encoder_pos_enc",
|
||||
_create_sinusoidal_position_embedding(1 + 1 + self.horizon, self.d_model).unsqueeze(0),
|
||||
)
|
||||
|
||||
# Backbone for image feature extraction.
|
||||
self.image_normalizer = transforms.Normalize(
|
||||
mean=cfg.image_normalization.mean, std=cfg.image_normalization.std
|
||||
)
|
||||
backbone_model = getattr(torchvision.models, cfg.backbone)(
|
||||
replace_stride_with_dilation=[False, False, cfg.dilation],
|
||||
pretrained=cfg.pretrained_backbone,
|
||||
norm_layer=FrozenBatchNorm2d,
|
||||
)
|
||||
# Note: The forward method of this returns a dict: {"feature_map": output}.
|
||||
self.backbone = IntermediateLayerGetter(backbone_model, return_layers={"layer4": "feature_map"})
|
||||
|
||||
# Transformer (acts as VAE decoder when training with the variational objective).
|
||||
self.encoder = _TransformerEncoder(num_layers=cfg.enc_layers, **transformer_common_kwargs)
|
||||
self.decoder = _TransformerDecoder(num_layers=cfg.dec_layers, **transformer_common_kwargs)
|
||||
|
||||
# Transformer encoder input projections. The tokens will be structured like
|
||||
# [latent, robot_state, image_feature_map_pixels].
|
||||
self.encoder_robot_state_input_proj = nn.Linear(cfg.state_dim, self.d_model)
|
||||
self.encoder_latent_input_proj = nn.Linear(self.latent_dim, self.d_model)
|
||||
self.encoder_img_feat_input_proj = nn.Conv2d(
|
||||
backbone_model.fc.in_features, self.d_model, kernel_size=1
|
||||
)
|
||||
# Transformer encoder positional embeddings.
|
||||
self.encoder_robot_and_latent_pos_embed = nn.Embedding(2, self.d_model)
|
||||
self.encoder_cam_feat_pos_embed = _SinusoidalPositionEmbedding2D(self.d_model // 2)
|
||||
|
||||
# Transformer decoder.
|
||||
# Learnable positional embedding for the transformer's decoder (in the style of DETR object queries).
|
||||
self.decoder_pos_embed = nn.Embedding(self.horizon, self.d_model)
|
||||
|
||||
# Final action regression head on the output of the transformer's decoder.
|
||||
self.action_head = nn.Linear(self.d_model, cfg.action_dim)
|
||||
|
||||
self._reset_parameters()
|
||||
|
||||
self._create_optimizer()
|
||||
self.to(self.device)
|
||||
|
||||
def _create_optimizer(self):
|
||||
optimizer_params_dicts = [
|
||||
{
|
||||
"params": [
|
||||
p for n, p in self.named_parameters() if not n.startswith("backbone") and p.requires_grad
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": [
|
||||
p for n, p in self.named_parameters() if n.startswith("backbone") and p.requires_grad
|
||||
],
|
||||
"lr": self.cfg.lr_backbone,
|
||||
},
|
||||
]
|
||||
self.optimizer = torch.optim.AdamW(
|
||||
optimizer_params_dicts, lr=self.cfg.lr, weight_decay=self.cfg.weight_decay
|
||||
)
|
||||
|
||||
def _reset_parameters(self):
|
||||
"""Xavier-uniform initialization of the transformer parameters as in the original code."""
|
||||
for p in chain(self.encoder.parameters(), self.decoder.parameters()):
|
||||
if p.dim() > 1:
|
||||
nn.init.xavier_uniform_(p)
|
||||
|
||||
def reset(self):
|
||||
"""This should be called whenever the environment is reset."""
|
||||
if self.n_action_steps is not None:
|
||||
self._action_queue = deque([], maxlen=self.n_action_steps)
|
||||
|
||||
def select_action(self, batch: dict[str, Tensor], *_, **__) -> Tensor:
|
||||
"""
|
||||
Clear observation and action queues. Should be called on `env.reset()`
|
||||
This method wraps `select_actions` in order to return one action at a time for execution in the
|
||||
environment. It works by managing the actions in a queue and only calling `select_actions` when the
|
||||
queue is empty.
|
||||
"""
|
||||
self._queues = {
|
||||
"observation.images.top": deque(maxlen=self.n_obs_steps),
|
||||
"observation.state": deque(maxlen=self.n_obs_steps),
|
||||
"action": deque(maxlen=self.n_action_steps),
|
||||
if len(self._action_queue) == 0:
|
||||
# `select_actions` returns a (batch_size, n_action_steps, *) tensor, but the queue effectively has shape
|
||||
# (n_action_steps, batch_size, *), hence the transpose.
|
||||
self._action_queue.extend(self.select_actions(batch).transpose(0, 1))
|
||||
return self._action_queue.popleft()
|
||||
|
||||
@torch.no_grad()
|
||||
def select_actions(self, batch: dict[str, Tensor]) -> Tensor:
|
||||
"""Use the action chunking transformer to generate a sequence of actions."""
|
||||
self.eval()
|
||||
self._preprocess_batch(batch, add_obs_steps_dim=True)
|
||||
|
||||
action = self.forward(batch, return_loss=False)
|
||||
|
||||
if self.cfg.temporal_agg:
|
||||
# TODO(rcadene): implement temporal aggregation
|
||||
raise NotImplementedError()
|
||||
# all_time_actions[[t], t:t+num_queries] = action
|
||||
# actions_for_curr_step = all_time_actions[:, t]
|
||||
# actions_populated = torch.all(actions_for_curr_step != 0, axis=1)
|
||||
# actions_for_curr_step = actions_for_curr_step[actions_populated]
|
||||
# k = 0.01
|
||||
# exp_weights = np.exp(-k * np.arange(len(actions_for_curr_step)))
|
||||
# exp_weights = exp_weights / exp_weights.sum()
|
||||
# exp_weights = torch.from_numpy(exp_weights).cuda().unsqueeze(dim=1)
|
||||
# raw_action = (actions_for_curr_step * exp_weights).sum(dim=0, keepdim=True)
|
||||
|
||||
return action[: self.n_action_steps]
|
||||
|
||||
def __call__(self, *args, **kwargs) -> dict:
|
||||
# TODO(now): Temporary bridge until we know what to do about the `update` method.
|
||||
return self.update(*args, **kwargs)
|
||||
|
||||
def _preprocess_batch(
|
||||
self, batch: dict[str, Tensor], add_obs_steps_dim: bool = False
|
||||
) -> dict[str, Tensor]:
|
||||
"""
|
||||
This function expects `batch` to have (at least):
|
||||
{
|
||||
"observation.state": (B, 1, J) OR (B, J) tensor of robot states (joint configuration).
|
||||
"observation.images.top": (B, 1, C, H, W) OR (B, C, H, W) tensor of images.
|
||||
"action": (B, H, J) tensor of actions (positional target for robot joint configuration)
|
||||
"action_is_pad": (B, H) mask for whether the actions are padding outside of the episode bounds.
|
||||
}
|
||||
"""
|
||||
if add_obs_steps_dim:
|
||||
# Add a dimension for the observations steps. Since n_obs_steps > 1 is not supported right now,
|
||||
# this just amounts to an unsqueeze.
|
||||
for k in batch:
|
||||
if k.startswith("observation."):
|
||||
batch[k] = batch[k].unsqueeze(1)
|
||||
|
||||
def forward(self, batch, step):
|
||||
del step
|
||||
if batch["observation.state"].shape[1] != 1:
|
||||
raise ValueError(self._multiple_obs_steps_not_handled_msg)
|
||||
batch["observation.state"] = batch["observation.state"].squeeze(1)
|
||||
# TODO(alexander-soare): generalize this to multiple images.
|
||||
assert (
|
||||
sum(k.startswith("observation.images.") and not k.endswith("is_pad") for k in batch) == 1
|
||||
), "ACT only handles one image for now."
|
||||
# Note: no squeeze is required for "observation.images.top" because then we'd have to unsqueeze to get
|
||||
# the image index dimension.
|
||||
|
||||
def update(self, batch, *_, **__) -> dict:
|
||||
start_time = time.time()
|
||||
self._preprocess_batch(batch)
|
||||
|
||||
self.train()
|
||||
|
||||
image = batch["observation.images.top"]
|
||||
# batch, num_cam, channel, height, width
|
||||
image = image.unsqueeze(1)
|
||||
assert image.ndim == 5
|
||||
num_slices = self.cfg.batch_size
|
||||
batch_size = self.cfg.horizon * num_slices
|
||||
|
||||
state = batch["observation.state"]
|
||||
# batch, qpos_dim
|
||||
assert state.ndim == 2
|
||||
assert batch_size % self.cfg.horizon == 0
|
||||
assert batch_size % num_slices == 0
|
||||
|
||||
action = batch["action"]
|
||||
# batch, seq, action_dim
|
||||
assert action.ndim == 3
|
||||
|
||||
preprocessed_batch = {
|
||||
"obs": {
|
||||
"image": image,
|
||||
"agent_pos": state,
|
||||
},
|
||||
"action": action,
|
||||
}
|
||||
|
||||
data_s = time.time() - start_time
|
||||
|
||||
loss = self.compute_loss(preprocessed_batch)
|
||||
loss = self.forward(batch, return_loss=True)["loss"]
|
||||
loss.backward()
|
||||
|
||||
grad_norm = torch.nn.utils.clip_grad_norm_(
|
||||
self.model.parameters(),
|
||||
self.parameters(),
|
||||
self.cfg.grad_clip_norm,
|
||||
error_if_nonfinite=False,
|
||||
)
|
||||
|
||||
self.optimizer.step()
|
||||
self.optimizer.zero_grad()
|
||||
# self.lr_scheduler.step()
|
||||
|
||||
info = {
|
||||
"loss": loss.item(),
|
||||
"grad_norm": float(grad_norm),
|
||||
# "lr": self.lr_scheduler.get_last_lr()[0],
|
||||
"lr": self.cfg.lr,
|
||||
"data_s": data_s,
|
||||
"update_s": time.time() - start_time,
|
||||
}
|
||||
|
||||
return info
|
||||
|
||||
def forward(self, batch: dict[str, Tensor], return_loss: bool = False) -> dict | Tensor:
|
||||
images = self.image_normalizer(batch["observation.images.top"])
|
||||
|
||||
if return_loss: # training time
|
||||
actions_hat, (mu_hat, log_sigma_x2_hat) = self._forward(
|
||||
batch["observation.state"], images, batch["action"]
|
||||
)
|
||||
|
||||
l1_loss = (
|
||||
F.l1_loss(batch["action"], actions_hat, reduction="none")
|
||||
* ~batch["action_is_pad"].unsqueeze(-1)
|
||||
).mean()
|
||||
|
||||
loss_dict = {}
|
||||
loss_dict["l1"] = l1_loss
|
||||
if self.cfg.use_vae:
|
||||
# Calculate Dₖₗ(latent_pdf || standard_normal). Note: After computing the KL-divergence for
|
||||
# each dimension independently, we sum over the latent dimension to get the total
|
||||
# KL-divergence per batch element, then take the mean over the batch.
|
||||
# (See App. B of https://arxiv.org/abs/1312.6114 for more details).
|
||||
mean_kld = (
|
||||
(-0.5 * (1 + log_sigma_x2_hat - mu_hat.pow(2) - (log_sigma_x2_hat).exp())).sum(-1).mean()
|
||||
)
|
||||
loss_dict["kl"] = mean_kld
|
||||
loss_dict["loss"] = loss_dict["l1"] + loss_dict["kl"] * self.cfg.kl_weight
|
||||
else:
|
||||
loss_dict["loss"] = loss_dict["l1"]
|
||||
return loss_dict
|
||||
else:
|
||||
action, _ = self._forward(batch["observation.state"], images)
|
||||
return action
|
||||
|
||||
def _forward(
|
||||
self, robot_state: Tensor, image: Tensor, actions: Tensor | None = None
|
||||
) -> tuple[Tensor, tuple[Tensor | None, Tensor | None]]:
|
||||
"""
|
||||
Args:
|
||||
robot_state: (B, J) batch of robot joint configurations.
|
||||
image: (B, N, C, H, W) batch of N camera frames.
|
||||
actions: (B, S, A) batch of actions from the target dataset which must be provided if the
|
||||
VAE is enabled and the model is in training mode.
|
||||
Returns:
|
||||
(B, S, A) batch of action sequences
|
||||
Tuple containing the latent PDF's parameters (mean, log(σ²)) both as (B, L) tensors where L is the
|
||||
latent dimension.
|
||||
"""
|
||||
if self.use_vae and self.training:
|
||||
assert (
|
||||
actions is not None
|
||||
), "actions must be provided when using the variational objective in training mode."
|
||||
|
||||
batch_size = robot_state.shape[0]
|
||||
|
||||
# Prepare the latent for input to the transformer encoder.
|
||||
if self.use_vae and actions is not None:
|
||||
# Prepare the input to the VAE encoder: [cls, *joint_space_configuration, *action_sequence].
|
||||
cls_embed = einops.repeat(
|
||||
self.vae_encoder_cls_embed.weight, "1 d -> b 1 d", b=batch_size
|
||||
) # (B, 1, D)
|
||||
robot_state_embed = self.vae_encoder_robot_state_input_proj(robot_state).unsqueeze(1) # (B, 1, D)
|
||||
action_embed = self.vae_encoder_action_input_proj(actions) # (B, S, D)
|
||||
vae_encoder_input = torch.cat([cls_embed, robot_state_embed, action_embed], axis=1) # (B, S+2, D)
|
||||
|
||||
# Prepare fixed positional embedding.
|
||||
# Note: detach() shouldn't be necessary but leaving it the same as the original code just in case.
|
||||
pos_embed = self.vae_encoder_pos_enc.clone().detach() # (1, S+2, D)
|
||||
|
||||
# Forward pass through VAE encoder to get the latent PDF parameters.
|
||||
cls_token_out = self.vae_encoder(
|
||||
vae_encoder_input.permute(1, 0, 2), pos_embed=pos_embed.permute(1, 0, 2)
|
||||
)[0] # select the class token, with shape (B, D)
|
||||
latent_pdf_params = self.vae_encoder_latent_output_proj(cls_token_out)
|
||||
mu = latent_pdf_params[:, : self.latent_dim]
|
||||
# This is 2log(sigma). Done this way to match the original implementation.
|
||||
log_sigma_x2 = latent_pdf_params[:, self.latent_dim :]
|
||||
|
||||
# Sample the latent with the reparameterization trick.
|
||||
latent_sample = mu + log_sigma_x2.div(2).exp() * torch.randn_like(mu)
|
||||
else:
|
||||
# When not using the VAE encoder, we set the latent to be all zeros.
|
||||
mu = log_sigma_x2 = None
|
||||
latent_sample = torch.zeros([batch_size, self.latent_dim], dtype=torch.float32).to(
|
||||
robot_state.device
|
||||
)
|
||||
|
||||
# Prepare all other transformer encoder inputs.
|
||||
# Camera observation features and positional embeddings.
|
||||
all_cam_features = []
|
||||
all_cam_pos_embeds = []
|
||||
for cam_id, _ in enumerate(self.camera_names):
|
||||
cam_features = self.backbone(image[:, cam_id])["feature_map"]
|
||||
cam_pos_embed = self.encoder_cam_feat_pos_embed(cam_features).to(dtype=cam_features.dtype)
|
||||
cam_features = self.encoder_img_feat_input_proj(cam_features) # (B, C, h, w)
|
||||
all_cam_features.append(cam_features)
|
||||
all_cam_pos_embeds.append(cam_pos_embed)
|
||||
# Concatenate camera observation feature maps and positional embeddings along the width dimension.
|
||||
encoder_in = torch.cat(all_cam_features, axis=3)
|
||||
cam_pos_embed = torch.cat(all_cam_pos_embeds, axis=3)
|
||||
|
||||
# Get positional embeddings for robot state and latent.
|
||||
robot_state_embed = self.encoder_robot_state_input_proj(robot_state)
|
||||
latent_embed = self.encoder_latent_input_proj(latent_sample)
|
||||
|
||||
# Stack encoder input and positional embeddings moving to (S, B, C).
|
||||
encoder_in = torch.cat(
|
||||
[
|
||||
torch.stack([latent_embed, robot_state_embed], axis=0),
|
||||
encoder_in.flatten(2).permute(2, 0, 1),
|
||||
]
|
||||
)
|
||||
pos_embed = torch.cat(
|
||||
[
|
||||
self.encoder_robot_and_latent_pos_embed.weight.unsqueeze(1),
|
||||
cam_pos_embed.flatten(2).permute(2, 0, 1),
|
||||
],
|
||||
axis=0,
|
||||
)
|
||||
|
||||
# Forward pass through the transformer modules.
|
||||
encoder_out = self.encoder(encoder_in, pos_embed=pos_embed)
|
||||
decoder_in = torch.zeros(
|
||||
(self.horizon, batch_size, self.d_model), dtype=pos_embed.dtype, device=pos_embed.device
|
||||
)
|
||||
decoder_out = self.decoder(
|
||||
decoder_in,
|
||||
encoder_out,
|
||||
encoder_pos_embed=pos_embed,
|
||||
decoder_pos_embed=self.decoder_pos_embed.weight.unsqueeze(1),
|
||||
)
|
||||
|
||||
# Move back to (B, S, C).
|
||||
decoder_out = decoder_out.transpose(0, 1)
|
||||
|
||||
actions = self.action_head(decoder_out)
|
||||
|
||||
return actions, (mu, log_sigma_x2)
|
||||
|
||||
def save(self, fp):
|
||||
torch.save(self.state_dict(), fp)
|
||||
|
||||
|
|
@ -127,89 +421,258 @@ class ActionChunkingTransformerPolicy(nn.Module):
|
|||
d = torch.load(fp)
|
||||
self.load_state_dict(d)
|
||||
|
||||
def compute_loss(self, batch):
|
||||
loss_dict = self._forward(
|
||||
qpos=batch["obs"]["agent_pos"],
|
||||
image=batch["obs"]["image"],
|
||||
actions=batch["action"],
|
||||
|
||||
class _TransformerEncoder(nn.Module):
|
||||
"""Convenience module for running multiple encoder layers, maybe followed by normalization."""
|
||||
|
||||
def __init__(self, num_layers: int, **encoder_layer_kwargs: dict):
|
||||
super().__init__()
|
||||
self.layers = nn.ModuleList(
|
||||
[_TransformerEncoderLayer(**encoder_layer_kwargs) for _ in range(num_layers)]
|
||||
)
|
||||
self.norm = (
|
||||
nn.LayerNorm(encoder_layer_kwargs["d_model"])
|
||||
if encoder_layer_kwargs["normalize_before"]
|
||||
else nn.Identity()
|
||||
)
|
||||
loss = loss_dict["loss"]
|
||||
return loss
|
||||
|
||||
@torch.no_grad()
|
||||
def select_action(self, batch, step):
|
||||
assert "observation.images.top" in batch
|
||||
assert "observation.state" in batch
|
||||
assert len(batch) == 2
|
||||
def forward(self, x: Tensor, pos_embed: Tensor | None = None) -> Tensor:
|
||||
for layer in self.layers:
|
||||
x = layer(x, pos_embed=pos_embed)
|
||||
x = self.norm(x)
|
||||
return x
|
||||
|
||||
self._queues = populate_queues(self._queues, batch)
|
||||
|
||||
# TODO(rcadene): remove unused step_count
|
||||
del step
|
||||
class _TransformerEncoderLayer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
d_model: int,
|
||||
num_heads: int,
|
||||
dim_feedforward: int,
|
||||
dropout: float,
|
||||
activation: str,
|
||||
normalize_before: bool,
|
||||
):
|
||||
super().__init__()
|
||||
self.self_attn = nn.MultiheadAttention(d_model, num_heads, dropout=dropout)
|
||||
|
||||
self.eval()
|
||||
# Feed forward layers.
|
||||
self.linear1 = nn.Linear(d_model, dim_feedforward)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
self.linear2 = nn.Linear(dim_feedforward, d_model)
|
||||
|
||||
if len(self._queues["action"]) == 0:
|
||||
batch = {key: torch.stack(list(self._queues[key]), dim=1) for key in batch}
|
||||
self.norm1 = nn.LayerNorm(d_model)
|
||||
self.norm2 = nn.LayerNorm(d_model)
|
||||
self.dropout1 = nn.Dropout(dropout)
|
||||
self.dropout2 = nn.Dropout(dropout)
|
||||
|
||||
if self.n_obs_steps == 1:
|
||||
# hack to remove the time dimension
|
||||
for key in batch:
|
||||
assert batch[key].shape[1] == 1
|
||||
batch[key] = batch[key][:, 0]
|
||||
self.activation = _get_activation_fn(activation)
|
||||
self.normalize_before = normalize_before
|
||||
|
||||
actions = self._forward(
|
||||
# TODO(rcadene): remove unsqueeze hack to add the "number of cameras" dimension
|
||||
image=batch["observation.images.top"].unsqueeze(1),
|
||||
qpos=batch["observation.state"],
|
||||
)
|
||||
|
||||
if self.cfg.temporal_agg:
|
||||
# TODO(rcadene): implement temporal aggregation
|
||||
raise NotImplementedError()
|
||||
# all_time_actions[[t], t:t+num_queries] = action
|
||||
# actions_for_curr_step = all_time_actions[:, t]
|
||||
# actions_populated = torch.all(actions_for_curr_step != 0, axis=1)
|
||||
# actions_for_curr_step = actions_for_curr_step[actions_populated]
|
||||
# k = 0.01
|
||||
# exp_weights = np.exp(-k * np.arange(len(actions_for_curr_step)))
|
||||
# exp_weights = exp_weights / exp_weights.sum()
|
||||
# exp_weights = torch.from_numpy(exp_weights).cuda().unsqueeze(dim=1)
|
||||
# raw_action = (actions_for_curr_step * exp_weights).sum(dim=0, keepdim=True)
|
||||
|
||||
# act returns a sequence of `n` actions, but we consider only
|
||||
# the first `n_action_steps` actions subset
|
||||
for i in range(self.n_action_steps):
|
||||
self._queues["action"].append(actions[:, i])
|
||||
|
||||
action = self._queues["action"].popleft()
|
||||
return action
|
||||
|
||||
def _forward(self, qpos, image, actions=None, is_pad=None):
|
||||
env_state = None
|
||||
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
|
||||
image = normalize(image)
|
||||
|
||||
is_training = actions is not None
|
||||
if is_training: # training time
|
||||
actions = actions[:, : self.model.num_queries]
|
||||
if is_pad is not None:
|
||||
is_pad = is_pad[:, : self.model.num_queries]
|
||||
|
||||
a_hat, is_pad_hat, (mu, logvar) = self.model(qpos, image, env_state, actions, is_pad)
|
||||
|
||||
all_l1 = F.l1_loss(actions, a_hat, reduction="none")
|
||||
l1 = all_l1.mean() if is_pad is None else (all_l1 * ~is_pad.unsqueeze(-1)).mean()
|
||||
|
||||
loss_dict = {}
|
||||
loss_dict["l1"] = l1
|
||||
if self.cfg.vae:
|
||||
total_kld, dim_wise_kld, mean_kld = kl_divergence(mu, logvar)
|
||||
loss_dict["kl"] = total_kld[0]
|
||||
loss_dict["loss"] = loss_dict["l1"] + loss_dict["kl"] * self.kl_weight
|
||||
else:
|
||||
loss_dict["loss"] = loss_dict["l1"]
|
||||
return loss_dict
|
||||
def forward(self, x, pos_embed: Tensor | None = None) -> Tensor:
|
||||
skip = x
|
||||
if self.normalize_before:
|
||||
x = self.norm1(x)
|
||||
q = k = x if pos_embed is None else x + pos_embed
|
||||
x = self.self_attn(q, k, value=x)[0] # select just the output, not the attention weights
|
||||
x = skip + self.dropout1(x)
|
||||
if self.normalize_before:
|
||||
skip = x
|
||||
x = self.norm2(x)
|
||||
else:
|
||||
action, _, (_, _) = self.model(qpos, image, env_state) # no action, sample from prior
|
||||
return action
|
||||
x = self.norm1(x)
|
||||
skip = x
|
||||
x = self.linear2(self.dropout(self.activation(self.linear1(x))))
|
||||
x = skip + self.dropout2(x)
|
||||
if not self.normalize_before:
|
||||
x = self.norm2(x)
|
||||
return x
|
||||
|
||||
|
||||
class _TransformerDecoder(nn.Module):
|
||||
def __init__(self, num_layers: int, **decoder_layer_kwargs):
|
||||
"""Convenience module for running multiple decoder layers followed by normalization."""
|
||||
super().__init__()
|
||||
self.layers = nn.ModuleList(
|
||||
[_TransformerDecoderLayer(**decoder_layer_kwargs) for _ in range(num_layers)]
|
||||
)
|
||||
self.num_layers = num_layers
|
||||
self.norm = nn.LayerNorm(decoder_layer_kwargs["d_model"])
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: Tensor,
|
||||
encoder_out: Tensor,
|
||||
decoder_pos_embed: Tensor | None = None,
|
||||
encoder_pos_embed: Tensor | None = None,
|
||||
) -> Tensor:
|
||||
for layer in self.layers:
|
||||
x = layer(
|
||||
x, encoder_out, decoder_pos_embed=decoder_pos_embed, encoder_pos_embed=encoder_pos_embed
|
||||
)
|
||||
if self.norm is not None:
|
||||
x = self.norm(x)
|
||||
return x
|
||||
|
||||
|
||||
class _TransformerDecoderLayer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
d_model: int,
|
||||
num_heads: int,
|
||||
dim_feedforward: int,
|
||||
dropout: float,
|
||||
activation: str,
|
||||
normalize_before: bool,
|
||||
):
|
||||
super().__init__()
|
||||
self.self_attn = nn.MultiheadAttention(d_model, num_heads, dropout=dropout)
|
||||
self.multihead_attn = nn.MultiheadAttention(d_model, num_heads, dropout=dropout)
|
||||
|
||||
# Feed forward layers.
|
||||
self.linear1 = nn.Linear(d_model, dim_feedforward)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
self.linear2 = nn.Linear(dim_feedforward, d_model)
|
||||
|
||||
self.norm1 = nn.LayerNorm(d_model)
|
||||
self.norm2 = nn.LayerNorm(d_model)
|
||||
self.norm3 = nn.LayerNorm(d_model)
|
||||
self.dropout1 = nn.Dropout(dropout)
|
||||
self.dropout2 = nn.Dropout(dropout)
|
||||
self.dropout3 = nn.Dropout(dropout)
|
||||
|
||||
self.activation = _get_activation_fn(activation)
|
||||
self.normalize_before = normalize_before
|
||||
|
||||
def maybe_add_pos_embed(self, tensor: Tensor, pos_embed: Tensor | None) -> Tensor:
|
||||
return tensor if pos_embed is None else tensor + pos_embed
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: Tensor,
|
||||
encoder_out: Tensor,
|
||||
decoder_pos_embed: Tensor | None = None,
|
||||
encoder_pos_embed: Tensor | None = None,
|
||||
) -> Tensor:
|
||||
"""
|
||||
Args:
|
||||
x: (Decoder Sequence, Batch, Channel) tensor of input tokens.
|
||||
encoder_out: (Encoder Sequence, B, C) output features from the last layer of the encoder we are
|
||||
cross-attending with.
|
||||
decoder_pos_embed: (ES, 1, C) positional embedding for keys (from the encoder).
|
||||
encoder_pos_embed: (DS, 1, C) Positional_embedding for the queries (from the decoder).
|
||||
Returns:
|
||||
(DS, B, C) tensor of decoder output features.
|
||||
"""
|
||||
skip = x
|
||||
if self.normalize_before:
|
||||
x = self.norm1(x)
|
||||
q = k = self.maybe_add_pos_embed(x, decoder_pos_embed)
|
||||
x = self.self_attn(q, k, value=x)[0] # select just the output, not the attention weights
|
||||
x = skip + self.dropout1(x)
|
||||
if self.normalize_before:
|
||||
skip = x
|
||||
x = self.norm2(x)
|
||||
else:
|
||||
x = self.norm1(x)
|
||||
skip = x
|
||||
x = self.multihead_attn(
|
||||
query=self.maybe_add_pos_embed(x, decoder_pos_embed),
|
||||
key=self.maybe_add_pos_embed(encoder_out, encoder_pos_embed),
|
||||
value=encoder_out,
|
||||
)[0] # select just the output, not the attention weights
|
||||
x = skip + self.dropout2(x)
|
||||
if self.normalize_before:
|
||||
skip = x
|
||||
x = self.norm3(x)
|
||||
else:
|
||||
x = self.norm2(x)
|
||||
skip = x
|
||||
x = self.linear2(self.dropout(self.activation(self.linear1(x))))
|
||||
x = skip + self.dropout3(x)
|
||||
if not self.normalize_before:
|
||||
x = self.norm3(x)
|
||||
return x
|
||||
|
||||
|
||||
def _create_sinusoidal_position_embedding(num_positions: int, dimension: int) -> Tensor:
|
||||
"""1D sinusoidal positional embeddings as in Attention is All You Need.
|
||||
|
||||
Args:
|
||||
num_positions: Number of token positions required.
|
||||
Returns: (num_positions, dimension) position embeddings (the first dimension is the batch dimension).
|
||||
|
||||
"""
|
||||
|
||||
def get_position_angle_vec(position):
|
||||
return [position / np.power(10000, 2 * (hid_j // 2) / dimension) for hid_j in range(dimension)]
|
||||
|
||||
sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(num_positions)])
|
||||
sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i
|
||||
sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1
|
||||
return torch.from_numpy(sinusoid_table).float()
|
||||
|
||||
|
||||
class _SinusoidalPositionEmbedding2D(nn.Module):
|
||||
"""2D sinusoidal positional embeddings similar to what's presented in Attention Is All You Need.
|
||||
|
||||
The variation is that the position indices are normalized in [0, 2π] (not quite: the lower bound is 1/H
|
||||
for the vertical direction, and 1/W for the horizontal direction.
|
||||
"""
|
||||
|
||||
def __init__(self, dimension: int):
|
||||
"""
|
||||
Args:
|
||||
dimension: The desired dimension of the embeddings.
|
||||
"""
|
||||
super().__init__()
|
||||
self.dimension = dimension
|
||||
self._two_pi = 2 * math.pi
|
||||
self._eps = 1e-6
|
||||
# Inverse "common ratio" for the geometric progression in sinusoid frequencies.
|
||||
self._temperature = 10000
|
||||
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
"""
|
||||
Args:
|
||||
x: A (B, C, H, W) batch of 2D feature map to generate the embeddings for.
|
||||
Returns:
|
||||
A (1, C, H, W) batch of corresponding sinusoidal positional embeddings.
|
||||
"""
|
||||
not_mask = torch.ones_like(x[0, :1]) # (1, H, W)
|
||||
# Note: These are like range(1, H+1) and range(1, W+1) respectively, but in most implementations
|
||||
# they would be range(0, H) and range(0, W). Keeping it at as is to match the original code.
|
||||
y_range = not_mask.cumsum(1, dtype=torch.float32)
|
||||
x_range = not_mask.cumsum(2, dtype=torch.float32)
|
||||
|
||||
# "Normalize" the position index such that it ranges in [0, 2π].
|
||||
# Note: Adding epsilon on the denominator should not be needed as all values of y_embed and x_range
|
||||
# are non-zero by construction. This is an artifact of the original code.
|
||||
y_range = y_range / (y_range[:, -1:, :] + self._eps) * self._two_pi
|
||||
x_range = x_range / (x_range[:, :, -1:] + self._eps) * self._two_pi
|
||||
|
||||
inverse_frequency = self._temperature ** (
|
||||
2 * (torch.arange(self.dimension, dtype=torch.float32, device=x.device) // 2) / self.dimension
|
||||
)
|
||||
|
||||
x_range = x_range.unsqueeze(-1) / inverse_frequency # (1, H, W, 1)
|
||||
y_range = y_range.unsqueeze(-1) / inverse_frequency # (1, H, W, 1)
|
||||
|
||||
# Note: this stack then flatten operation results in interleaved sine and cosine terms.
|
||||
# pos_embed_x and pos_embed_y are (1, H, W, C // 2).
|
||||
pos_embed_x = torch.stack((x_range[..., 0::2].sin(), x_range[..., 1::2].cos()), dim=-1).flatten(3)
|
||||
pos_embed_y = torch.stack((y_range[..., 0::2].sin(), y_range[..., 1::2].cos()), dim=-1).flatten(3)
|
||||
pos_embed = torch.cat((pos_embed_y, pos_embed_x), dim=3).permute(0, 3, 1, 2) # (1, C, H, W)
|
||||
|
||||
return pos_embed
|
||||
|
||||
|
||||
def _get_activation_fn(activation: str) -> Callable:
|
||||
"""Return an activation function given a string."""
|
||||
if activation == "relu":
|
||||
return F.relu
|
||||
if activation == "gelu":
|
||||
return F.gelu
|
||||
if activation == "glu":
|
||||
return F.glu
|
||||
raise RuntimeError(f"activation should be relu/gelu/glu, not {activation}.")
|
||||
|
|
|
|||
|
|
@ -1,102 +0,0 @@
|
|||
"""
|
||||
Various positional encodings for the transformer.
|
||||
"""
|
||||
|
||||
import math
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from .utils import NestedTensor
|
||||
|
||||
|
||||
class PositionEmbeddingSine(nn.Module):
|
||||
"""
|
||||
This is a more standard version of the position embedding, very similar to the one
|
||||
used by the Attention is all you need paper, generalized to work on images.
|
||||
"""
|
||||
|
||||
def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
|
||||
super().__init__()
|
||||
self.num_pos_feats = num_pos_feats
|
||||
self.temperature = temperature
|
||||
self.normalize = normalize
|
||||
if scale is not None and normalize is False:
|
||||
raise ValueError("normalize should be True if scale is passed")
|
||||
if scale is None:
|
||||
scale = 2 * math.pi
|
||||
self.scale = scale
|
||||
|
||||
def forward(self, tensor):
|
||||
x = tensor
|
||||
# mask = tensor_list.mask
|
||||
# assert mask is not None
|
||||
# not_mask = ~mask
|
||||
|
||||
not_mask = torch.ones_like(x[0, [0]])
|
||||
y_embed = not_mask.cumsum(1, dtype=torch.float32)
|
||||
x_embed = not_mask.cumsum(2, dtype=torch.float32)
|
||||
if self.normalize:
|
||||
eps = 1e-6
|
||||
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
|
||||
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
|
||||
|
||||
dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
|
||||
dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
|
||||
|
||||
pos_x = x_embed[:, :, :, None] / dim_t
|
||||
pos_y = y_embed[:, :, :, None] / dim_t
|
||||
pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
|
||||
pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
|
||||
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
|
||||
return pos
|
||||
|
||||
|
||||
class PositionEmbeddingLearned(nn.Module):
|
||||
"""
|
||||
Absolute pos embedding, learned.
|
||||
"""
|
||||
|
||||
def __init__(self, num_pos_feats=256):
|
||||
super().__init__()
|
||||
self.row_embed = nn.Embedding(50, num_pos_feats)
|
||||
self.col_embed = nn.Embedding(50, num_pos_feats)
|
||||
self.reset_parameters()
|
||||
|
||||
def reset_parameters(self):
|
||||
nn.init.uniform_(self.row_embed.weight)
|
||||
nn.init.uniform_(self.col_embed.weight)
|
||||
|
||||
def forward(self, tensor_list: NestedTensor):
|
||||
x = tensor_list.tensors
|
||||
h, w = x.shape[-2:]
|
||||
i = torch.arange(w, device=x.device)
|
||||
j = torch.arange(h, device=x.device)
|
||||
x_emb = self.col_embed(i)
|
||||
y_emb = self.row_embed(j)
|
||||
pos = (
|
||||
torch.cat(
|
||||
[
|
||||
x_emb.unsqueeze(0).repeat(h, 1, 1),
|
||||
y_emb.unsqueeze(1).repeat(1, w, 1),
|
||||
],
|
||||
dim=-1,
|
||||
)
|
||||
.permute(2, 0, 1)
|
||||
.unsqueeze(0)
|
||||
.repeat(x.shape[0], 1, 1, 1)
|
||||
)
|
||||
return pos
|
||||
|
||||
|
||||
def build_position_encoding(args):
|
||||
n_steps = args.hidden_dim // 2
|
||||
if args.position_embedding in ("v2", "sine"):
|
||||
# TODO find a better way of exposing other arguments
|
||||
position_embedding = PositionEmbeddingSine(n_steps, normalize=True)
|
||||
elif args.position_embedding in ("v3", "learned"):
|
||||
position_embedding = PositionEmbeddingLearned(n_steps)
|
||||
else:
|
||||
raise ValueError(f"not supported {args.position_embedding}")
|
||||
|
||||
return position_embedding
|
||||
|
|
@ -1,371 +0,0 @@
|
|||
"""
|
||||
DETR Transformer class.
|
||||
|
||||
Copy-paste from torch.nn.Transformer with modifications:
|
||||
* positional encodings are passed in MHattention
|
||||
* extra LN at the end of encoder is removed
|
||||
* decoder returns a stack of activations from all decoding layers
|
||||
"""
|
||||
|
||||
import copy
|
||||
from typing import Optional
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F # noqa: N812
|
||||
from torch import Tensor, nn
|
||||
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
d_model=512,
|
||||
nhead=8,
|
||||
num_encoder_layers=6,
|
||||
num_decoder_layers=6,
|
||||
dim_feedforward=2048,
|
||||
dropout=0.1,
|
||||
activation="relu",
|
||||
normalize_before=False,
|
||||
return_intermediate_dec=False,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
encoder_layer = TransformerEncoderLayer(
|
||||
d_model, nhead, dim_feedforward, dropout, activation, normalize_before
|
||||
)
|
||||
encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
|
||||
self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
|
||||
|
||||
decoder_layer = TransformerDecoderLayer(
|
||||
d_model, nhead, dim_feedforward, dropout, activation, normalize_before
|
||||
)
|
||||
decoder_norm = nn.LayerNorm(d_model)
|
||||
self.decoder = TransformerDecoder(
|
||||
decoder_layer, num_decoder_layers, decoder_norm, return_intermediate=return_intermediate_dec
|
||||
)
|
||||
|
||||
self._reset_parameters()
|
||||
|
||||
self.d_model = d_model
|
||||
self.nhead = nhead
|
||||
|
||||
def _reset_parameters(self):
|
||||
for p in self.parameters():
|
||||
if p.dim() > 1:
|
||||
nn.init.xavier_uniform_(p)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
src,
|
||||
mask,
|
||||
query_embed,
|
||||
pos_embed,
|
||||
latent_input=None,
|
||||
proprio_input=None,
|
||||
additional_pos_embed=None,
|
||||
):
|
||||
# TODO flatten only when input has H and W
|
||||
if len(src.shape) == 4: # has H and W
|
||||
# flatten NxCxHxW to HWxNxC
|
||||
bs, c, h, w = src.shape
|
||||
src = src.flatten(2).permute(2, 0, 1)
|
||||
pos_embed = pos_embed.flatten(2).permute(2, 0, 1).repeat(1, bs, 1)
|
||||
query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)
|
||||
# mask = mask.flatten(1)
|
||||
|
||||
additional_pos_embed = additional_pos_embed.unsqueeze(1).repeat(1, bs, 1) # seq, bs, dim
|
||||
pos_embed = torch.cat([additional_pos_embed, pos_embed], axis=0)
|
||||
|
||||
addition_input = torch.stack([latent_input, proprio_input], axis=0)
|
||||
src = torch.cat([addition_input, src], axis=0)
|
||||
else:
|
||||
assert len(src.shape) == 3
|
||||
# flatten NxHWxC to HWxNxC
|
||||
bs, hw, c = src.shape
|
||||
src = src.permute(1, 0, 2)
|
||||
pos_embed = pos_embed.unsqueeze(1).repeat(1, bs, 1)
|
||||
query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)
|
||||
|
||||
tgt = torch.zeros_like(query_embed)
|
||||
memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)
|
||||
hs = self.decoder(tgt, memory, memory_key_padding_mask=mask, pos=pos_embed, query_pos=query_embed)
|
||||
hs = hs.transpose(1, 2)
|
||||
return hs
|
||||
|
||||
|
||||
class TransformerEncoder(nn.Module):
|
||||
def __init__(self, encoder_layer, num_layers, norm=None):
|
||||
super().__init__()
|
||||
self.layers = _get_clones(encoder_layer, num_layers)
|
||||
self.num_layers = num_layers
|
||||
self.norm = norm
|
||||
|
||||
def forward(
|
||||
self,
|
||||
src,
|
||||
mask: Optional[Tensor] = None,
|
||||
src_key_padding_mask: Optional[Tensor] = None,
|
||||
pos: Optional[Tensor] = None,
|
||||
):
|
||||
output = src
|
||||
|
||||
for layer in self.layers:
|
||||
output = layer(output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, pos=pos)
|
||||
|
||||
if self.norm is not None:
|
||||
output = self.norm(output)
|
||||
|
||||
return output
|
||||
|
||||
|
||||
class TransformerDecoder(nn.Module):
|
||||
def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):
|
||||
super().__init__()
|
||||
self.layers = _get_clones(decoder_layer, num_layers)
|
||||
self.num_layers = num_layers
|
||||
self.norm = norm
|
||||
self.return_intermediate = return_intermediate
|
||||
|
||||
def forward(
|
||||
self,
|
||||
tgt,
|
||||
memory,
|
||||
tgt_mask: Optional[Tensor] = None,
|
||||
memory_mask: Optional[Tensor] = None,
|
||||
tgt_key_padding_mask: Optional[Tensor] = None,
|
||||
memory_key_padding_mask: Optional[Tensor] = None,
|
||||
pos: Optional[Tensor] = None,
|
||||
query_pos: Optional[Tensor] = None,
|
||||
):
|
||||
output = tgt
|
||||
|
||||
intermediate = []
|
||||
|
||||
for layer in self.layers:
|
||||
output = layer(
|
||||
output,
|
||||
memory,
|
||||
tgt_mask=tgt_mask,
|
||||
memory_mask=memory_mask,
|
||||
tgt_key_padding_mask=tgt_key_padding_mask,
|
||||
memory_key_padding_mask=memory_key_padding_mask,
|
||||
pos=pos,
|
||||
query_pos=query_pos,
|
||||
)
|
||||
if self.return_intermediate:
|
||||
intermediate.append(self.norm(output))
|
||||
|
||||
if self.norm is not None:
|
||||
output = self.norm(output)
|
||||
if self.return_intermediate:
|
||||
intermediate.pop()
|
||||
intermediate.append(output)
|
||||
|
||||
if self.return_intermediate:
|
||||
return torch.stack(intermediate)
|
||||
|
||||
return output.unsqueeze(0)
|
||||
|
||||
|
||||
class TransformerEncoderLayer(nn.Module):
|
||||
def __init__(
|
||||
self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", normalize_before=False
|
||||
):
|
||||
super().__init__()
|
||||
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
|
||||
# Implementation of Feedforward model
|
||||
self.linear1 = nn.Linear(d_model, dim_feedforward)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
self.linear2 = nn.Linear(dim_feedforward, d_model)
|
||||
|
||||
self.norm1 = nn.LayerNorm(d_model)
|
||||
self.norm2 = nn.LayerNorm(d_model)
|
||||
self.dropout1 = nn.Dropout(dropout)
|
||||
self.dropout2 = nn.Dropout(dropout)
|
||||
|
||||
self.activation = _get_activation_fn(activation)
|
||||
self.normalize_before = normalize_before
|
||||
|
||||
def with_pos_embed(self, tensor, pos: Optional[Tensor]):
|
||||
return tensor if pos is None else tensor + pos
|
||||
|
||||
def forward_post(
|
||||
self,
|
||||
src,
|
||||
src_mask: Optional[Tensor] = None,
|
||||
src_key_padding_mask: Optional[Tensor] = None,
|
||||
pos: Optional[Tensor] = None,
|
||||
):
|
||||
q = k = self.with_pos_embed(src, pos)
|
||||
src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
|
||||
src = src + self.dropout1(src2)
|
||||
src = self.norm1(src)
|
||||
src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
|
||||
src = src + self.dropout2(src2)
|
||||
src = self.norm2(src)
|
||||
return src
|
||||
|
||||
def forward_pre(
|
||||
self,
|
||||
src,
|
||||
src_mask: Optional[Tensor] = None,
|
||||
src_key_padding_mask: Optional[Tensor] = None,
|
||||
pos: Optional[Tensor] = None,
|
||||
):
|
||||
src2 = self.norm1(src)
|
||||
q = k = self.with_pos_embed(src2, pos)
|
||||
src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
|
||||
src = src + self.dropout1(src2)
|
||||
src2 = self.norm2(src)
|
||||
src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))
|
||||
src = src + self.dropout2(src2)
|
||||
return src
|
||||
|
||||
def forward(
|
||||
self,
|
||||
src,
|
||||
src_mask: Optional[Tensor] = None,
|
||||
src_key_padding_mask: Optional[Tensor] = None,
|
||||
pos: Optional[Tensor] = None,
|
||||
):
|
||||
if self.normalize_before:
|
||||
return self.forward_pre(src, src_mask, src_key_padding_mask, pos)
|
||||
return self.forward_post(src, src_mask, src_key_padding_mask, pos)
|
||||
|
||||
|
||||
class TransformerDecoderLayer(nn.Module):
|
||||
def __init__(
|
||||
self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", normalize_before=False
|
||||
):
|
||||
super().__init__()
|
||||
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
|
||||
self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
|
||||
# Implementation of Feedforward model
|
||||
self.linear1 = nn.Linear(d_model, dim_feedforward)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
self.linear2 = nn.Linear(dim_feedforward, d_model)
|
||||
|
||||
self.norm1 = nn.LayerNorm(d_model)
|
||||
self.norm2 = nn.LayerNorm(d_model)
|
||||
self.norm3 = nn.LayerNorm(d_model)
|
||||
self.dropout1 = nn.Dropout(dropout)
|
||||
self.dropout2 = nn.Dropout(dropout)
|
||||
self.dropout3 = nn.Dropout(dropout)
|
||||
|
||||
self.activation = _get_activation_fn(activation)
|
||||
self.normalize_before = normalize_before
|
||||
|
||||
def with_pos_embed(self, tensor, pos: Optional[Tensor]):
|
||||
return tensor if pos is None else tensor + pos
|
||||
|
||||
def forward_post(
|
||||
self,
|
||||
tgt,
|
||||
memory,
|
||||
tgt_mask: Optional[Tensor] = None,
|
||||
memory_mask: Optional[Tensor] = None,
|
||||
tgt_key_padding_mask: Optional[Tensor] = None,
|
||||
memory_key_padding_mask: Optional[Tensor] = None,
|
||||
pos: Optional[Tensor] = None,
|
||||
query_pos: Optional[Tensor] = None,
|
||||
):
|
||||
q = k = self.with_pos_embed(tgt, query_pos)
|
||||
tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0]
|
||||
tgt = tgt + self.dropout1(tgt2)
|
||||
tgt = self.norm1(tgt)
|
||||
tgt2 = self.multihead_attn(
|
||||
query=self.with_pos_embed(tgt, query_pos),
|
||||
key=self.with_pos_embed(memory, pos),
|
||||
value=memory,
|
||||
attn_mask=memory_mask,
|
||||
key_padding_mask=memory_key_padding_mask,
|
||||
)[0]
|
||||
tgt = tgt + self.dropout2(tgt2)
|
||||
tgt = self.norm2(tgt)
|
||||
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
|
||||
tgt = tgt + self.dropout3(tgt2)
|
||||
tgt = self.norm3(tgt)
|
||||
return tgt
|
||||
|
||||
def forward_pre(
|
||||
self,
|
||||
tgt,
|
||||
memory,
|
||||
tgt_mask: Optional[Tensor] = None,
|
||||
memory_mask: Optional[Tensor] = None,
|
||||
tgt_key_padding_mask: Optional[Tensor] = None,
|
||||
memory_key_padding_mask: Optional[Tensor] = None,
|
||||
pos: Optional[Tensor] = None,
|
||||
query_pos: Optional[Tensor] = None,
|
||||
):
|
||||
tgt2 = self.norm1(tgt)
|
||||
q = k = self.with_pos_embed(tgt2, query_pos)
|
||||
tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0]
|
||||
tgt = tgt + self.dropout1(tgt2)
|
||||
tgt2 = self.norm2(tgt)
|
||||
tgt2 = self.multihead_attn(
|
||||
query=self.with_pos_embed(tgt2, query_pos),
|
||||
key=self.with_pos_embed(memory, pos),
|
||||
value=memory,
|
||||
attn_mask=memory_mask,
|
||||
key_padding_mask=memory_key_padding_mask,
|
||||
)[0]
|
||||
tgt = tgt + self.dropout2(tgt2)
|
||||
tgt2 = self.norm3(tgt)
|
||||
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
|
||||
tgt = tgt + self.dropout3(tgt2)
|
||||
return tgt
|
||||
|
||||
def forward(
|
||||
self,
|
||||
tgt,
|
||||
memory,
|
||||
tgt_mask: Optional[Tensor] = None,
|
||||
memory_mask: Optional[Tensor] = None,
|
||||
tgt_key_padding_mask: Optional[Tensor] = None,
|
||||
memory_key_padding_mask: Optional[Tensor] = None,
|
||||
pos: Optional[Tensor] = None,
|
||||
query_pos: Optional[Tensor] = None,
|
||||
):
|
||||
if self.normalize_before:
|
||||
return self.forward_pre(
|
||||
tgt,
|
||||
memory,
|
||||
tgt_mask,
|
||||
memory_mask,
|
||||
tgt_key_padding_mask,
|
||||
memory_key_padding_mask,
|
||||
pos,
|
||||
query_pos,
|
||||
)
|
||||
return self.forward_post(
|
||||
tgt, memory, tgt_mask, memory_mask, tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos
|
||||
)
|
||||
|
||||
|
||||
def _get_clones(module, n):
|
||||
return nn.ModuleList([copy.deepcopy(module) for _ in range(n)])
|
||||
|
||||
|
||||
def build_transformer(args):
|
||||
return Transformer(
|
||||
d_model=args.hidden_dim,
|
||||
dropout=args.dropout,
|
||||
nhead=args.nheads,
|
||||
dim_feedforward=args.dim_feedforward,
|
||||
num_encoder_layers=args.enc_layers,
|
||||
num_decoder_layers=args.dec_layers,
|
||||
normalize_before=args.pre_norm,
|
||||
return_intermediate_dec=True,
|
||||
)
|
||||
|
||||
|
||||
def _get_activation_fn(activation):
|
||||
"""Return an activation function given a string"""
|
||||
if activation == "relu":
|
||||
return F.relu
|
||||
if activation == "gelu":
|
||||
return F.gelu
|
||||
if activation == "glu":
|
||||
return F.glu
|
||||
raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
|
||||
|
|
@ -1,478 +0,0 @@
|
|||
"""
|
||||
Misc functions, including distributed helpers.
|
||||
|
||||
Mostly copy-paste from torchvision references.
|
||||
"""
|
||||
|
||||
import datetime
|
||||
import os
|
||||
import pickle
|
||||
import subprocess
|
||||
import time
|
||||
from collections import defaultdict, deque
|
||||
from typing import List, Optional
|
||||
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
|
||||
# needed due to empty tensor bug in pytorch and torchvision 0.5
|
||||
import torchvision
|
||||
from packaging import version
|
||||
from torch import Tensor
|
||||
|
||||
if version.parse(torchvision.__version__) < version.parse("0.7"):
|
||||
from torchvision.ops import _new_empty_tensor
|
||||
from torchvision.ops.misc import _output_size
|
||||
|
||||
|
||||
class SmoothedValue:
|
||||
"""Track a series of values and provide access to smoothed values over a
|
||||
window or the global series average.
|
||||
"""
|
||||
|
||||
def __init__(self, window_size=20, fmt=None):
|
||||
if fmt is None:
|
||||
fmt = "{median:.4f} ({global_avg:.4f})"
|
||||
self.deque = deque(maxlen=window_size)
|
||||
self.total = 0.0
|
||||
self.count = 0
|
||||
self.fmt = fmt
|
||||
|
||||
def update(self, value, n=1):
|
||||
self.deque.append(value)
|
||||
self.count += n
|
||||
self.total += value * n
|
||||
|
||||
def synchronize_between_processes(self):
|
||||
"""
|
||||
Warning: does not synchronize the deque!
|
||||
"""
|
||||
if not is_dist_avail_and_initialized():
|
||||
return
|
||||
t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda")
|
||||
dist.barrier()
|
||||
dist.all_reduce(t)
|
||||
t = t.tolist()
|
||||
self.count = int(t[0])
|
||||
self.total = t[1]
|
||||
|
||||
@property
|
||||
def median(self):
|
||||
d = torch.tensor(list(self.deque))
|
||||
return d.median().item()
|
||||
|
||||
@property
|
||||
def avg(self):
|
||||
d = torch.tensor(list(self.deque), dtype=torch.float32)
|
||||
return d.mean().item()
|
||||
|
||||
@property
|
||||
def global_avg(self):
|
||||
return self.total / self.count
|
||||
|
||||
@property
|
||||
def max(self):
|
||||
return max(self.deque)
|
||||
|
||||
@property
|
||||
def value(self):
|
||||
return self.deque[-1]
|
||||
|
||||
def __str__(self):
|
||||
return self.fmt.format(
|
||||
median=self.median, avg=self.avg, global_avg=self.global_avg, max=self.max, value=self.value
|
||||
)
|
||||
|
||||
|
||||
def all_gather(data):
|
||||
"""
|
||||
Run all_gather on arbitrary picklable data (not necessarily tensors)
|
||||
Args:
|
||||
data: any picklable object
|
||||
Returns:
|
||||
list[data]: list of data gathered from each rank
|
||||
"""
|
||||
world_size = get_world_size()
|
||||
if world_size == 1:
|
||||
return [data]
|
||||
|
||||
# serialized to a Tensor
|
||||
buffer = pickle.dumps(data)
|
||||
storage = torch.ByteStorage.from_buffer(buffer)
|
||||
tensor = torch.ByteTensor(storage).to("cuda")
|
||||
|
||||
# obtain Tensor size of each rank
|
||||
local_size = torch.tensor([tensor.numel()], device="cuda")
|
||||
size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)]
|
||||
dist.all_gather(size_list, local_size)
|
||||
size_list = [int(size.item()) for size in size_list]
|
||||
max_size = max(size_list)
|
||||
|
||||
# receiving Tensor from all ranks
|
||||
# we pad the tensor because torch all_gather does not support
|
||||
# gathering tensors of different shapes
|
||||
tensor_list = []
|
||||
for _ in size_list:
|
||||
tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda"))
|
||||
if local_size != max_size:
|
||||
padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda")
|
||||
tensor = torch.cat((tensor, padding), dim=0)
|
||||
dist.all_gather(tensor_list, tensor)
|
||||
|
||||
data_list = []
|
||||
for size, tensor in zip(size_list, tensor_list, strict=False):
|
||||
buffer = tensor.cpu().numpy().tobytes()[:size]
|
||||
data_list.append(pickle.loads(buffer))
|
||||
|
||||
return data_list
|
||||
|
||||
|
||||
def reduce_dict(input_dict, average=True):
|
||||
"""
|
||||
Args:
|
||||
input_dict (dict): all the values will be reduced
|
||||
average (bool): whether to do average or sum
|
||||
Reduce the values in the dictionary from all processes so that all processes
|
||||
have the averaged results. Returns a dict with the same fields as
|
||||
input_dict, after reduction.
|
||||
"""
|
||||
world_size = get_world_size()
|
||||
if world_size < 2:
|
||||
return input_dict
|
||||
with torch.no_grad():
|
||||
names = []
|
||||
values = []
|
||||
# sort the keys so that they are consistent across processes
|
||||
for k in sorted(input_dict.keys()):
|
||||
names.append(k)
|
||||
values.append(input_dict[k])
|
||||
values = torch.stack(values, dim=0)
|
||||
dist.all_reduce(values)
|
||||
if average:
|
||||
values /= world_size
|
||||
reduced_dict = {k: v for k, v in zip(names, values, strict=False)} # noqa: C416
|
||||
return reduced_dict
|
||||
|
||||
|
||||
class MetricLogger:
|
||||
def __init__(self, delimiter="\t"):
|
||||
self.meters = defaultdict(SmoothedValue)
|
||||
self.delimiter = delimiter
|
||||
|
||||
def update(self, **kwargs):
|
||||
for k, v in kwargs.items():
|
||||
if isinstance(v, torch.Tensor):
|
||||
v = v.item()
|
||||
assert isinstance(v, (float, int))
|
||||
self.meters[k].update(v)
|
||||
|
||||
def __getattr__(self, attr):
|
||||
if attr in self.meters:
|
||||
return self.meters[attr]
|
||||
if attr in self.__dict__:
|
||||
return self.__dict__[attr]
|
||||
raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr))
|
||||
|
||||
def __str__(self):
|
||||
loss_str = []
|
||||
for name, meter in self.meters.items():
|
||||
loss_str.append("{}: {}".format(name, str(meter)))
|
||||
return self.delimiter.join(loss_str)
|
||||
|
||||
def synchronize_between_processes(self):
|
||||
for meter in self.meters.values():
|
||||
meter.synchronize_between_processes()
|
||||
|
||||
def add_meter(self, name, meter):
|
||||
self.meters[name] = meter
|
||||
|
||||
def log_every(self, iterable, print_freq, header=None):
|
||||
if not header:
|
||||
header = ""
|
||||
start_time = time.time()
|
||||
end = time.time()
|
||||
iter_time = SmoothedValue(fmt="{avg:.4f}")
|
||||
data_time = SmoothedValue(fmt="{avg:.4f}")
|
||||
space_fmt = ":" + str(len(str(len(iterable)))) + "d"
|
||||
if torch.cuda.is_available():
|
||||
log_msg = self.delimiter.join(
|
||||
[
|
||||
header,
|
||||
"[{0" + space_fmt + "}/{1}]",
|
||||
"eta: {eta}",
|
||||
"{meters}",
|
||||
"time: {time}",
|
||||
"data: {data}",
|
||||
"max mem: {memory:.0f}",
|
||||
]
|
||||
)
|
||||
else:
|
||||
log_msg = self.delimiter.join(
|
||||
[
|
||||
header,
|
||||
"[{0" + space_fmt + "}/{1}]",
|
||||
"eta: {eta}",
|
||||
"{meters}",
|
||||
"time: {time}",
|
||||
"data: {data}",
|
||||
]
|
||||
)
|
||||
mega_b = 1024.0 * 1024.0
|
||||
for i, obj in enumerate(iterable):
|
||||
data_time.update(time.time() - end)
|
||||
yield obj
|
||||
iter_time.update(time.time() - end)
|
||||
if i % print_freq == 0 or i == len(iterable) - 1:
|
||||
eta_seconds = iter_time.global_avg * (len(iterable) - i)
|
||||
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
|
||||
if torch.cuda.is_available():
|
||||
print(
|
||||
log_msg.format(
|
||||
i,
|
||||
len(iterable),
|
||||
eta=eta_string,
|
||||
meters=str(self),
|
||||
time=str(iter_time),
|
||||
data=str(data_time),
|
||||
memory=torch.cuda.max_memory_allocated() / mega_b,
|
||||
)
|
||||
)
|
||||
else:
|
||||
print(
|
||||
log_msg.format(
|
||||
i,
|
||||
len(iterable),
|
||||
eta=eta_string,
|
||||
meters=str(self),
|
||||
time=str(iter_time),
|
||||
data=str(data_time),
|
||||
)
|
||||
)
|
||||
end = time.time()
|
||||
total_time = time.time() - start_time
|
||||
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
|
||||
print("{} Total time: {} ({:.4f} s / it)".format(header, total_time_str, total_time / len(iterable)))
|
||||
|
||||
|
||||
def get_sha():
|
||||
cwd = os.path.dirname(os.path.abspath(__file__))
|
||||
|
||||
def _run(command):
|
||||
return subprocess.check_output(command, cwd=cwd).decode("ascii").strip()
|
||||
|
||||
sha = "N/A"
|
||||
diff = "clean"
|
||||
branch = "N/A"
|
||||
try:
|
||||
sha = _run(["git", "rev-parse", "HEAD"])
|
||||
subprocess.check_output(["git", "diff"], cwd=cwd)
|
||||
diff = _run(["git", "diff-index", "HEAD"])
|
||||
diff = "has uncommited changes" if diff else "clean"
|
||||
branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"])
|
||||
except Exception:
|
||||
pass
|
||||
message = f"sha: {sha}, status: {diff}, branch: {branch}"
|
||||
return message
|
||||
|
||||
|
||||
def collate_fn(batch):
|
||||
batch = list(zip(*batch, strict=False))
|
||||
batch[0] = nested_tensor_from_tensor_list(batch[0])
|
||||
return tuple(batch)
|
||||
|
||||
|
||||
def _max_by_axis(the_list):
|
||||
# type: (List[List[int]]) -> List[int]
|
||||
maxes = the_list[0]
|
||||
for sublist in the_list[1:]:
|
||||
for index, item in enumerate(sublist):
|
||||
maxes[index] = max(maxes[index], item)
|
||||
return maxes
|
||||
|
||||
|
||||
class NestedTensor:
|
||||
def __init__(self, tensors, mask: Optional[Tensor]):
|
||||
self.tensors = tensors
|
||||
self.mask = mask
|
||||
|
||||
def to(self, device):
|
||||
# type: (Device) -> NestedTensor # noqa
|
||||
cast_tensor = self.tensors.to(device)
|
||||
mask = self.mask
|
||||
if mask is not None:
|
||||
assert mask is not None
|
||||
cast_mask = mask.to(device)
|
||||
else:
|
||||
cast_mask = None
|
||||
return NestedTensor(cast_tensor, cast_mask)
|
||||
|
||||
def decompose(self):
|
||||
return self.tensors, self.mask
|
||||
|
||||
def __repr__(self):
|
||||
return str(self.tensors)
|
||||
|
||||
|
||||
def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
|
||||
# TODO make this more general
|
||||
if tensor_list[0].ndim == 3:
|
||||
if torchvision._is_tracing():
|
||||
# nested_tensor_from_tensor_list() does not export well to ONNX
|
||||
# call _onnx_nested_tensor_from_tensor_list() instead
|
||||
return _onnx_nested_tensor_from_tensor_list(tensor_list)
|
||||
|
||||
# TODO make it support different-sized images
|
||||
max_size = _max_by_axis([list(img.shape) for img in tensor_list])
|
||||
# min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
|
||||
batch_shape = [len(tensor_list)] + max_size
|
||||
b, c, h, w = batch_shape
|
||||
dtype = tensor_list[0].dtype
|
||||
device = tensor_list[0].device
|
||||
tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
|
||||
mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
|
||||
for img, pad_img, m in zip(tensor_list, tensor, mask, strict=False):
|
||||
pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
|
||||
m[: img.shape[1], : img.shape[2]] = False
|
||||
else:
|
||||
raise ValueError("not supported")
|
||||
return NestedTensor(tensor, mask)
|
||||
|
||||
|
||||
# _onnx_nested_tensor_from_tensor_list() is an implementation of
|
||||
# nested_tensor_from_tensor_list() that is supported by ONNX tracing.
|
||||
@torch.jit.unused
|
||||
def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
|
||||
max_size = []
|
||||
for i in range(tensor_list[0].dim()):
|
||||
max_size_i = torch.max(torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)).to(
|
||||
torch.int64
|
||||
)
|
||||
max_size.append(max_size_i)
|
||||
max_size = tuple(max_size)
|
||||
|
||||
# work around for
|
||||
# pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
|
||||
# m[: img.shape[1], :img.shape[2]] = False
|
||||
# which is not yet supported in onnx
|
||||
padded_imgs = []
|
||||
padded_masks = []
|
||||
for img in tensor_list:
|
||||
padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape), strict=False)]
|
||||
padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
|
||||
padded_imgs.append(padded_img)
|
||||
|
||||
m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
|
||||
padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
|
||||
padded_masks.append(padded_mask.to(torch.bool))
|
||||
|
||||
tensor = torch.stack(padded_imgs)
|
||||
mask = torch.stack(padded_masks)
|
||||
|
||||
return NestedTensor(tensor, mask=mask)
|
||||
|
||||
|
||||
def setup_for_distributed(is_master):
|
||||
"""
|
||||
This function disables printing when not in master process
|
||||
"""
|
||||
import builtins as __builtin__
|
||||
|
||||
builtin_print = __builtin__.print
|
||||
|
||||
def print(*args, **kwargs):
|
||||
force = kwargs.pop("force", False)
|
||||
if is_master or force:
|
||||
builtin_print(*args, **kwargs)
|
||||
|
||||
__builtin__.print = print
|
||||
|
||||
|
||||
def is_dist_avail_and_initialized():
|
||||
if not dist.is_available():
|
||||
return False
|
||||
if not dist.is_initialized():
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def get_world_size():
|
||||
if not is_dist_avail_and_initialized():
|
||||
return 1
|
||||
return dist.get_world_size()
|
||||
|
||||
|
||||
def get_rank():
|
||||
if not is_dist_avail_and_initialized():
|
||||
return 0
|
||||
return dist.get_rank()
|
||||
|
||||
|
||||
def is_main_process():
|
||||
return get_rank() == 0
|
||||
|
||||
|
||||
def save_on_master(*args, **kwargs):
|
||||
if is_main_process():
|
||||
torch.save(*args, **kwargs)
|
||||
|
||||
|
||||
def init_distributed_mode(args):
|
||||
if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
|
||||
args.rank = int(os.environ["RANK"])
|
||||
args.world_size = int(os.environ["WORLD_SIZE"])
|
||||
args.gpu = int(os.environ["LOCAL_RANK"])
|
||||
elif "SLURM_PROCID" in os.environ:
|
||||
args.rank = int(os.environ["SLURM_PROCID"])
|
||||
args.gpu = args.rank % torch.cuda.device_count()
|
||||
else:
|
||||
print("Not using distributed mode")
|
||||
args.distributed = False
|
||||
return
|
||||
|
||||
args.distributed = True
|
||||
|
||||
torch.cuda.set_device(args.gpu)
|
||||
args.dist_backend = "nccl"
|
||||
print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True)
|
||||
torch.distributed.init_process_group(
|
||||
backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank
|
||||
)
|
||||
torch.distributed.barrier()
|
||||
setup_for_distributed(args.rank == 0)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def accuracy(output, target, topk=(1,)):
|
||||
"""Computes the precision@k for the specified values of k"""
|
||||
if target.numel() == 0:
|
||||
return [torch.zeros([], device=output.device)]
|
||||
maxk = max(topk)
|
||||
batch_size = target.size(0)
|
||||
|
||||
_, pred = output.topk(maxk, 1, True, True)
|
||||
pred = pred.t()
|
||||
correct = pred.eq(target.view(1, -1).expand_as(pred))
|
||||
|
||||
res = []
|
||||
for k in topk:
|
||||
correct_k = correct[:k].view(-1).float().sum(0)
|
||||
res.append(correct_k.mul_(100.0 / batch_size))
|
||||
return res
|
||||
|
||||
|
||||
def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
|
||||
# type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor
|
||||
"""
|
||||
Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
|
||||
This will eventually be supported natively by PyTorch, and this
|
||||
class can go away.
|
||||
"""
|
||||
if version.parse(torchvision.__version__) < version.parse("0.7"):
|
||||
if input.numel() > 0:
|
||||
return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners)
|
||||
|
||||
output_shape = _output_size(2, input, size, scale_factor)
|
||||
output_shape = list(input.shape[:-2]) + list(output_shape)
|
||||
return _new_empty_tensor(input, output_shape)
|
||||
else:
|
||||
return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)
|
||||
|
|
@ -151,8 +151,6 @@ class DiffusionPolicy(nn.Module):
|
|||
|
||||
self.diffusion.train()
|
||||
|
||||
data_s = time.time() - start_time
|
||||
|
||||
loss = self.diffusion.compute_loss(batch)
|
||||
loss.backward()
|
||||
|
||||
|
|
@ -173,7 +171,6 @@ class DiffusionPolicy(nn.Module):
|
|||
"loss": loss.item(),
|
||||
"grad_norm": float(grad_norm),
|
||||
"lr": self.lr_scheduler.get_last_lr()[0],
|
||||
"data_s": data_s,
|
||||
"update_s": time.time() - start_time,
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -23,11 +23,7 @@ def make_policy(cfg):
|
|||
elif cfg.policy.name == "act":
|
||||
from lerobot.common.policies.act.policy import ActionChunkingTransformerPolicy
|
||||
|
||||
policy = ActionChunkingTransformerPolicy(
|
||||
cfg.policy,
|
||||
n_obs_steps=cfg.policy.n_obs_steps,
|
||||
n_action_steps=cfg.policy.n_action_steps,
|
||||
)
|
||||
policy = ActionChunkingTransformerPolicy(cfg.policy, cfg.device)
|
||||
policy.to(cfg.device)
|
||||
else:
|
||||
raise ValueError(cfg.policy.name)
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
# @package _global_
|
||||
|
||||
offline_steps: 1344000
|
||||
offline_steps: 80000
|
||||
online_steps: 0
|
||||
|
||||
eval_episodes: 1
|
||||
|
|
@ -20,26 +20,27 @@ policy:
|
|||
|
||||
lr: 1e-5
|
||||
lr_backbone: 1e-5
|
||||
pretrained_backbone: true
|
||||
weight_decay: 1e-4
|
||||
grad_clip_norm: 10
|
||||
backbone: resnet18
|
||||
num_queries: ${horizon} # chunk_size
|
||||
horizon: ${horizon} # chunk_size
|
||||
kl_weight: 10
|
||||
hidden_dim: 512
|
||||
d_model: 512
|
||||
dim_feedforward: 3200
|
||||
vae_enc_layers: 4
|
||||
enc_layers: 4
|
||||
dec_layers: 7
|
||||
nheads: 8
|
||||
dec_layers: 1
|
||||
num_heads: 8
|
||||
#camera_names: [top, front_close, left_pillar, right_pillar]
|
||||
camera_names: [top]
|
||||
position_embedding: sine
|
||||
masks: false
|
||||
dilation: false
|
||||
dropout: 0.1
|
||||
pre_norm: false
|
||||
activation: relu
|
||||
latent_dim: 32
|
||||
|
||||
vae: true
|
||||
use_vae: true
|
||||
|
||||
batch_size: 8
|
||||
|
||||
|
|
@ -54,8 +55,14 @@ policy:
|
|||
|
||||
temporal_agg: false
|
||||
|
||||
state_dim: ???
|
||||
action_dim: ???
|
||||
state_dim: 14
|
||||
action_dim: 14
|
||||
|
||||
image_normalization:
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
|
||||
delta_timestamps:
|
||||
observation.images.top: [0.0]
|
||||
observation.state: [0.0]
|
||||
action: "[i / ${fps} for i in range(${horizon})]"
|
||||
|
|
|
|||
|
|
@ -307,7 +307,7 @@ def eval(cfg: dict, out_dir=None, stats_path=None):
|
|||
|
||||
logging.info("Making transforms.")
|
||||
# TODO(alexander-soare): Completely decouple datasets from evaluation.
|
||||
dataset = make_dataset(cfg, stats_path=stats_path)
|
||||
transform = make_dataset(cfg, stats_path=stats_path).transform
|
||||
|
||||
logging.info("Making environment.")
|
||||
env = make_env(cfg, num_parallel_envs=cfg.eval_episodes)
|
||||
|
|
@ -322,7 +322,7 @@ def eval(cfg: dict, out_dir=None, stats_path=None):
|
|||
video_dir=Path(out_dir) / "eval",
|
||||
fps=cfg.env.fps,
|
||||
# TODO(rcadene): what should we do with the transform?
|
||||
transform=dataset.transform,
|
||||
transform=transform,
|
||||
seed=cfg.seed,
|
||||
)
|
||||
print(info["aggregated"])
|
||||
|
|
|
|||
|
|
@ -41,7 +41,6 @@ def log_train_info(logger, info, step, cfg, dataset, is_offline):
|
|||
loss = info["loss"]
|
||||
grad_norm = info["grad_norm"]
|
||||
lr = info["lr"]
|
||||
data_s = info["data_s"]
|
||||
update_s = info["update_s"]
|
||||
|
||||
# A sample is an (observation,action) pair, where observation and action
|
||||
|
|
@ -62,7 +61,6 @@ def log_train_info(logger, info, step, cfg, dataset, is_offline):
|
|||
f"grdn:{grad_norm:.3f}",
|
||||
f"lr:{lr:0.1e}",
|
||||
# in seconds
|
||||
f"data_s:{data_s:.3f}",
|
||||
f"updt_s:{update_s:.3f}",
|
||||
]
|
||||
logging.info(" ".join(log_items))
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "absl-py"
|
||||
|
|
@ -521,7 +521,7 @@ files = [
|
|||
name = "dm-control"
|
||||
version = "1.0.14"
|
||||
description = "Continuous control environments and MuJoCo Python bindings."
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "dm_control-1.0.14-py3-none-any.whl", hash = "sha256:883c63244a7ebf598700a97564ed19fffd3479ca79efd090aed881609cdb9fc6"},
|
||||
|
|
@ -552,7 +552,7 @@ hdf5 = ["h5py"]
|
|||
name = "dm-env"
|
||||
version = "1.6"
|
||||
description = "A Python interface for Reinforcement Learning environments."
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "dm-env-1.6.tar.gz", hash = "sha256:a436eb1c654c39e0c986a516cee218bea7140b510fceff63f97eb4fcff3d93de"},
|
||||
|
|
@ -568,7 +568,7 @@ numpy = "*"
|
|||
name = "dm-tree"
|
||||
version = "0.1.8"
|
||||
description = "Tree is a library for working with nested data structures."
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "dm-tree-0.1.8.tar.gz", hash = "sha256:0fcaabbb14e7980377439e7140bd05552739ca5e515ecb3119f234acee4b9430"},
|
||||
|
|
@ -692,18 +692,18 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "filelock"
|
||||
version = "3.13.1"
|
||||
version = "3.13.3"
|
||||
description = "A platform independent file lock."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "filelock-3.13.1-py3-none-any.whl", hash = "sha256:57dbda9b35157b05fb3e58ee91448612eb674172fab98ee235ccb0b5bee19a1c"},
|
||||
{file = "filelock-3.13.1.tar.gz", hash = "sha256:521f5f56c50f8426f5e03ad3b281b490a87ef15bc6c526f168290f0c7148d44e"},
|
||||
{file = "filelock-3.13.3-py3-none-any.whl", hash = "sha256:5ffa845303983e7a0b7ae17636509bc97997d58afeafa72fb141a17b152284cb"},
|
||||
{file = "filelock-3.13.3.tar.gz", hash = "sha256:a79895a25bbefdf55d1a2a0a80968f7dbb28edcd6d4234a0afb3f37ecde4b546"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.24)"]
|
||||
testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
|
||||
docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
|
||||
testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
|
||||
typing = ["typing-extensions (>=4.8)"]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -777,26 +777,27 @@ smmap = ">=3.0.1,<6"
|
|||
|
||||
[[package]]
|
||||
name = "gitpython"
|
||||
version = "3.1.42"
|
||||
version = "3.1.43"
|
||||
description = "GitPython is a Python library used to interact with Git repositories"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "GitPython-3.1.42-py3-none-any.whl", hash = "sha256:1bf9cd7c9e7255f77778ea54359e54ac22a72a5b51288c457c881057b7bb9ecd"},
|
||||
{file = "GitPython-3.1.42.tar.gz", hash = "sha256:2d99869e0fef71a73cbd242528105af1d6c1b108c60dfabd994bf292f76c3ceb"},
|
||||
{file = "GitPython-3.1.43-py3-none-any.whl", hash = "sha256:eec7ec56b92aad751f9912a73404bc02ba212a23adb2c7098ee668417051a1ff"},
|
||||
{file = "GitPython-3.1.43.tar.gz", hash = "sha256:35f314a9f878467f5453cc1fee295c3e18e52f1b99f10f6cf5b1682e968a9e7c"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
gitdb = ">=4.0.1,<5"
|
||||
|
||||
[package.extras]
|
||||
test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar"]
|
||||
doc = ["sphinx (==4.3.2)", "sphinx-autodoc-typehints", "sphinx-rtd-theme", "sphinxcontrib-applehelp (>=1.0.2,<=1.0.4)", "sphinxcontrib-devhelp (==1.0.2)", "sphinxcontrib-htmlhelp (>=2.0.0,<=2.0.1)", "sphinxcontrib-qthelp (==1.0.3)", "sphinxcontrib-serializinghtml (==1.1.5)"]
|
||||
test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
|
||||
|
||||
[[package]]
|
||||
name = "glfw"
|
||||
version = "2.7.0"
|
||||
description = "A ctypes-based wrapper for GLFW3."
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "glfw-2.7.0-py2.py27.py3.py30.py31.py32.py33.py34.py35.py36.py37.py38-none-macosx_10_6_intel.whl", hash = "sha256:bd82849edcceda4e262bd1227afaa74b94f9f0731c1197863cd25c15bfc613fc"},
|
||||
|
|
@ -920,7 +921,7 @@ shapely = "^2.0.3"
|
|||
type = "git"
|
||||
url = "git@github.com:huggingface/gym-pusht.git"
|
||||
reference = "HEAD"
|
||||
resolved_reference = "6c9893504f670ff069d0f759a733e971ea1efdbf"
|
||||
resolved_reference = "824b22832cc8d71a4b4e96a57563510cf47e30c1"
|
||||
|
||||
[[package]]
|
||||
name = "gym-xarm"
|
||||
|
|
@ -940,7 +941,7 @@ mujoco = "^2.3.7"
|
|||
type = "git"
|
||||
url = "git@github.com:huggingface/gym-xarm.git"
|
||||
reference = "HEAD"
|
||||
resolved_reference = "08ddd5a9400783a6898bbf3c3014fc5da3961b9d"
|
||||
resolved_reference = "ce294c0d30def08414d9237e2bf9f373d448ca07"
|
||||
|
||||
[[package]]
|
||||
name = "gymnasium"
|
||||
|
|
@ -976,7 +977,7 @@ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
|
|||
name = "gymnasium-robotics"
|
||||
version = "1.2.4"
|
||||
description = "Robotics environments for the Gymnasium repo."
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "gymnasium-robotics-1.2.4.tar.gz", hash = "sha256:d304192b066f8b800599dfbe3d9d90bba9b761ee884472bdc4d05968a8bc61cb"},
|
||||
|
|
@ -1281,7 +1282,7 @@ i18n = ["Babel (>=2.7)"]
|
|||
name = "labmaze"
|
||||
version = "1.0.6"
|
||||
description = "LabMaze: DeepMind Lab's text maze generator."
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "labmaze-1.0.6-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:b2ddef976dfd8d992b19cfa6c633f2eba7576d759c2082da534e3f727479a84a"},
|
||||
|
|
@ -1325,7 +1326,7 @@ setuptools = "!=50.0.0"
|
|||
name = "lazy-loader"
|
||||
version = "0.3"
|
||||
description = "lazy_loader"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "lazy_loader-0.3-py3-none-any.whl", hash = "sha256:1e9e76ee8631e264c62ce10006718e80b2cfc74340d17d1031e0f84af7478554"},
|
||||
|
|
@ -1368,96 +1369,174 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "lxml"
|
||||
version = "5.1.0"
|
||||
version = "5.2.1"
|
||||
description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "lxml-5.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:704f5572ff473a5f897745abebc6df40f22d4133c1e0a1f124e4f2bd3330ff7e"},
|
||||
{file = "lxml-5.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9d3c0f8567ffe7502d969c2c1b809892dc793b5d0665f602aad19895f8d508da"},
|
||||
{file = "lxml-5.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5fcfbebdb0c5d8d18b84118842f31965d59ee3e66996ac842e21f957eb76138c"},
|
||||
{file = "lxml-5.1.0-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2f37c6d7106a9d6f0708d4e164b707037b7380fcd0b04c5bd9cae1fb46a856fb"},
|
||||
{file = "lxml-5.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2befa20a13f1a75c751f47e00929fb3433d67eb9923c2c0b364de449121f447c"},
|
||||
{file = "lxml-5.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:22b7ee4c35f374e2c20337a95502057964d7e35b996b1c667b5c65c567d2252a"},
|
||||
{file = "lxml-5.1.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:bf8443781533b8d37b295016a4b53c1494fa9a03573c09ca5104550c138d5c05"},
|
||||
{file = "lxml-5.1.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:82bddf0e72cb2af3cbba7cec1d2fd11fda0de6be8f4492223d4a268713ef2147"},
|
||||
{file = "lxml-5.1.0-cp310-cp310-win32.whl", hash = "sha256:b66aa6357b265670bb574f050ffceefb98549c721cf28351b748be1ef9577d93"},
|
||||
{file = "lxml-5.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:4946e7f59b7b6a9e27bef34422f645e9a368cb2be11bf1ef3cafc39a1f6ba68d"},
|
||||
{file = "lxml-5.1.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:14deca1460b4b0f6b01f1ddc9557704e8b365f55c63070463f6c18619ebf964f"},
|
||||
{file = "lxml-5.1.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:ed8c3d2cd329bf779b7ed38db176738f3f8be637bb395ce9629fc76f78afe3d4"},
|
||||
{file = "lxml-5.1.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:436a943c2900bb98123b06437cdd30580a61340fbdb7b28aaf345a459c19046a"},
|
||||
{file = "lxml-5.1.0-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:acb6b2f96f60f70e7f34efe0c3ea34ca63f19ca63ce90019c6cbca6b676e81fa"},
|
||||
{file = "lxml-5.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:af8920ce4a55ff41167ddbc20077f5698c2e710ad3353d32a07d3264f3a2021e"},
|
||||
{file = "lxml-5.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7cfced4a069003d8913408e10ca8ed092c49a7f6cefee9bb74b6b3e860683b45"},
|
||||
{file = "lxml-5.1.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:9e5ac3437746189a9b4121db2a7b86056ac8786b12e88838696899328fc44bb2"},
|
||||
{file = "lxml-5.1.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:f4c9bda132ad108b387c33fabfea47866af87f4ea6ffb79418004f0521e63204"},
|
||||
{file = "lxml-5.1.0-cp311-cp311-win32.whl", hash = "sha256:bc64d1b1dab08f679fb89c368f4c05693f58a9faf744c4d390d7ed1d8223869b"},
|
||||
{file = "lxml-5.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:a5ab722ae5a873d8dcee1f5f45ddd93c34210aed44ff2dc643b5025981908cda"},
|
||||
{file = "lxml-5.1.0-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:9aa543980ab1fbf1720969af1d99095a548ea42e00361e727c58a40832439114"},
|
||||
{file = "lxml-5.1.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:6f11b77ec0979f7e4dc5ae081325a2946f1fe424148d3945f943ceaede98adb8"},
|
||||
{file = "lxml-5.1.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:a36c506e5f8aeb40680491d39ed94670487ce6614b9d27cabe45d94cd5d63e1e"},
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||||
{file = "lxml-5.1.0-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f643ffd2669ffd4b5a3e9b41c909b72b2a1d5e4915da90a77e119b8d48ce867a"},
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||||
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||||
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||||
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||||
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||||
{file = "lxml-5.1.0-cp312-cp312-win32.whl", hash = "sha256:13521a321a25c641b9ea127ef478b580b5ec82aa2e9fc076c86169d161798b01"},
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
{file = "lxml-5.2.1-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f8aca2e3a72f37bfc7b14ba96d4056244001ddcc18382bd0daa087fd2e68a354"},
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||||
{file = "lxml-5.2.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5ca1e8188b26a819387b29c3895c47a5e618708fe6f787f3b1a471de2c4a94d9"},
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||||
{file = "lxml-5.2.1.tar.gz", hash = "sha256:3f7765e69bbce0906a7c74d5fe46d2c7a7596147318dbc08e4a2431f3060e306"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
cssselect = ["cssselect (>=0.7)"]
|
||||
html-clean = ["lxml-html-clean"]
|
||||
html5 = ["html5lib"]
|
||||
htmlsoup = ["BeautifulSoup4"]
|
||||
source = ["Cython (>=3.0.7)"]
|
||||
source = ["Cython (>=3.0.10)"]
|
||||
|
||||
[[package]]
|
||||
name = "markdown"
|
||||
|
|
@ -1588,7 +1667,7 @@ tests = ["pytest (>=4.6)"]
|
|||
name = "mujoco"
|
||||
version = "2.3.7"
|
||||
description = "MuJoCo Physics Simulator"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "mujoco-2.3.7-cp310-cp310-macosx_10_16_x86_64.whl", hash = "sha256:e8714a5ff6a1561b364b7b4648d4c0c8d13e751874cf7401c309b9d23fa9598b"},
|
||||
|
|
@ -1626,20 +1705,20 @@ pyopengl = "*"
|
|||
|
||||
[[package]]
|
||||
name = "networkx"
|
||||
version = "3.2.1"
|
||||
version = "3.3"
|
||||
description = "Python package for creating and manipulating graphs and networks"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
python-versions = ">=3.10"
|
||||
files = [
|
||||
{file = "networkx-3.2.1-py3-none-any.whl", hash = "sha256:f18c69adc97877c42332c170849c96cefa91881c99a7cb3e95b7c659ebdc1ec2"},
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|
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]
|
||||
|
||||
[package.extras]
|
||||
default = ["matplotlib (>=3.5)", "numpy (>=1.22)", "pandas (>=1.4)", "scipy (>=1.9,!=1.11.0,!=1.11.1)"]
|
||||
developer = ["changelist (==0.4)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"]
|
||||
doc = ["nb2plots (>=0.7)", "nbconvert (<7.9)", "numpydoc (>=1.6)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.14)", "sphinx (>=7)", "sphinx-gallery (>=0.14)", "texext (>=0.6.7)"]
|
||||
extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.11)", "sympy (>=1.10)"]
|
||||
default = ["matplotlib (>=3.6)", "numpy (>=1.23)", "pandas (>=1.4)", "scipy (>=1.9,!=1.11.0,!=1.11.1)"]
|
||||
developer = ["changelist (==0.5)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"]
|
||||
doc = ["myst-nb (>=1.0)", "numpydoc (>=1.7)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.14)", "sphinx (>=7)", "sphinx-gallery (>=0.14)", "texext (>=0.6.7)"]
|
||||
extra = ["lxml (>=4.6)", "pydot (>=2.0)", "pygraphviz (>=1.12)", "sympy (>=1.10)"]
|
||||
test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -1896,13 +1975,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "nvidia-nvjitlink-cu12"
|
||||
version = "12.4.99"
|
||||
version = "12.4.127"
|
||||
description = "Nvidia JIT LTO Library"
|
||||
optional = false
|
||||
python-versions = ">=3"
|
||||
files = [
|
||||
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||||
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|
||||
|
||||
[[package]]
|
||||
|
|
@ -2043,7 +2122,7 @@ xml = ["lxml (>=4.9.2)"]
|
|||
name = "pettingzoo"
|
||||
version = "1.24.3"
|
||||
description = "Gymnasium for multi-agent reinforcement learning."
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
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||||
|
|
@ -2066,79 +2145,80 @@ testing = ["AutoROM", "pre-commit", "pynput", "pytest", "pytest-cov", "pytest-ma
|
|||
|
||||
[[package]]
|
||||
name = "pillow"
|
||||
version = "10.2.0"
|
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version = "10.3.0"
|
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description = "Python Imaging Library (Fork)"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
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[package.extras]
|
||||
|
|
@ -2181,13 +2261,13 @@ testing = ["pytest", "pytest-benchmark"]
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|||
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||||
[[package]]
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||||
name = "pre-commit"
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version = "3.6.2"
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version = "3.7.0"
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||||
description = "A framework for managing and maintaining multi-language pre-commit hooks."
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optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "pre_commit-3.6.2-py2.py3-none-any.whl", hash = "sha256:ba637c2d7a670c10daedc059f5c49b5bd0aadbccfcd7ec15592cf9665117532c"},
|
||||
{file = "pre_commit-3.6.2.tar.gz", hash = "sha256:c3ef34f463045c88658c5b99f38c1e297abdcc0ff13f98d3370055fbbfabc67e"},
|
||||
{file = "pre_commit-3.7.0-py2.py3-none-any.whl", hash = "sha256:5eae9e10c2b5ac51577c3452ec0a490455c45a0533f7960f993a0d01e59decab"},
|
||||
{file = "pre_commit-3.7.0.tar.gz", hash = "sha256:e209d61b8acdcf742404408531f0c37d49d2c734fd7cff2d6076083d191cb060"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2261,13 +2341,13 @@ test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
|
|||
|
||||
[[package]]
|
||||
name = "pycparser"
|
||||
version = "2.21"
|
||||
version = "2.22"
|
||||
description = "C parser in Python"
|
||||
optional = false
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pycparser-2.21-py2.py3-none-any.whl", hash = "sha256:8ee45429555515e1f6b185e78100aea234072576aa43ab53aefcae078162fca9"},
|
||||
{file = "pycparser-2.21.tar.gz", hash = "sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206"},
|
||||
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|
||||
{file = "pycparser-2.22.tar.gz", hash = "sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -2411,7 +2491,7 @@ dev = ["aafigure", "matplotlib", "pygame", "pyglet (<2.0.0)", "sphinx", "wheel"]
|
|||
name = "pyopengl"
|
||||
version = "3.1.7"
|
||||
description = "Standard OpenGL bindings for Python"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "PyOpenGL-3.1.7-py3-none-any.whl", hash = "sha256:a6ab19cf290df6101aaf7470843a9c46207789855746399d0af92521a0a92b7a"},
|
||||
|
|
@ -2422,7 +2502,7 @@ files = [
|
|||
name = "pyparsing"
|
||||
version = "3.1.2"
|
||||
description = "pyparsing module - Classes and methods to define and execute parsing grammars"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.6.8"
|
||||
files = [
|
||||
{file = "pyparsing-3.1.2-py3-none-any.whl", hash = "sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742"},
|
||||
|
|
@ -2853,7 +2933,7 @@ torch = ["safetensors[numpy]", "torch (>=1.10)"]
|
|||
name = "scikit-image"
|
||||
version = "0.22.0"
|
||||
description = "Image processing in Python"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "scikit_image-0.22.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:74ec5c1d4693506842cc7c9487c89d8fc32aed064e9363def7af08b8f8cbb31d"},
|
||||
|
|
@ -2899,55 +2979,55 @@ test = ["asv", "matplotlib (>=3.5)", "numpydoc (>=1.5)", "pooch (>=1.6.0)", "pyt
|
|||
|
||||
[[package]]
|
||||
name = "scipy"
|
||||
version = "1.12.0"
|
||||
version = "1.13.0"
|
||||
description = "Fundamental algorithms for scientific computing in Python"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "scipy-1.12.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:78e4402e140879387187f7f25d91cc592b3501a2e51dfb320f48dfb73565f10b"},
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{file = "scipy-1.12.0-cp310-cp310-win_amd64.whl", hash = "sha256:75ea2a144096b5e39402e2ff53a36fecfd3b960d786b7efd3c180e29c39e53f2"},
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{file = "scipy-1.12.0-cp311-cp311-win_amd64.whl", hash = "sha256:a24024d45ce9a675c1fb8494e8e5244efea1c7a09c60beb1eeb80373d0fecc70"},
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{file = "scipy-1.12.0-cp39-cp39-win_amd64.whl", hash = "sha256:b360f1b6b2f742781299514e99ff560d1fe9bd1bff2712894b52abe528d1fd1e"},
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||||
{file = "scipy-1.12.0.tar.gz", hash = "sha256:4bf5abab8a36d20193c698b0f1fc282c1d083c94723902c447e5d2f1780936a3"},
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||||
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{file = "scipy-1.13.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:05f1432ba070e90d42d7fd836462c50bf98bd08bed0aa616c359eed8a04e3922"},
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{file = "scipy-1.13.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b8434f6f3fa49f631fae84afee424e2483289dfc30a47755b4b4e6b07b2633a4"},
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||||
{file = "scipy-1.13.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:dcbb9ea49b0167de4167c40eeee6e167caeef11effb0670b554d10b1e693a8b9"},
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||||
{file = "scipy-1.13.0-cp310-cp310-win_amd64.whl", hash = "sha256:1d2f7bb14c178f8b13ebae93f67e42b0a6b0fc50eba1cd8021c9b6e08e8fb1cd"},
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{file = "scipy-1.13.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0fbcf8abaf5aa2dc8d6400566c1a727aed338b5fe880cde64907596a89d576fa"},
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{file = "scipy-1.13.0-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:5e4a756355522eb60fcd61f8372ac2549073c8788f6114449b37e9e8104f15a5"},
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{file = "scipy-1.13.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b5acd8e1dbd8dbe38d0004b1497019b2dbbc3d70691e65d69615f8a7292865d7"},
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{file = "scipy-1.13.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9ff7dad5d24a8045d836671e082a490848e8639cabb3dbdacb29f943a678683d"},
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{file = "scipy-1.13.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:4dca18c3ffee287ddd3bc8f1dabaf45f5305c5afc9f8ab9cbfab855e70b2df5c"},
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{file = "scipy-1.13.0-cp311-cp311-win_amd64.whl", hash = "sha256:a2f471de4d01200718b2b8927f7d76b5d9bde18047ea0fa8bd15c5ba3f26a1d6"},
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||||
{file = "scipy-1.13.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:d0de696f589681c2802f9090fff730c218f7c51ff49bf252b6a97ec4a5d19e8b"},
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{file = "scipy-1.13.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:b2a3ff461ec4756b7e8e42e1c681077349a038f0686132d623fa404c0bee2551"},
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{file = "scipy-1.13.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6bf9fe63e7a4bf01d3645b13ff2aa6dea023d38993f42aaac81a18b1bda7a82a"},
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{file = "scipy-1.13.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1e7626dfd91cdea5714f343ce1176b6c4745155d234f1033584154f60ef1ff42"},
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||||
{file = "scipy-1.13.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:109d391d720fcebf2fbe008621952b08e52907cf4c8c7efc7376822151820820"},
|
||||
{file = "scipy-1.13.0-cp312-cp312-win_amd64.whl", hash = "sha256:8930ae3ea371d6b91c203b1032b9600d69c568e537b7988a3073dfe4d4774f21"},
|
||||
{file = "scipy-1.13.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:5407708195cb38d70fd2d6bb04b1b9dd5c92297d86e9f9daae1576bd9e06f602"},
|
||||
{file = "scipy-1.13.0-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:ac38c4c92951ac0f729c4c48c9e13eb3675d9986cc0c83943784d7390d540c78"},
|
||||
{file = "scipy-1.13.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:09c74543c4fbeb67af6ce457f6a6a28e5d3739a87f62412e4a16e46f164f0ae5"},
|
||||
{file = "scipy-1.13.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:28e286bf9ac422d6beb559bc61312c348ca9b0f0dae0d7c5afde7f722d6ea13d"},
|
||||
{file = "scipy-1.13.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:33fde20efc380bd23a78a4d26d59fc8704e9b5fd9b08841693eb46716ba13d86"},
|
||||
{file = "scipy-1.13.0-cp39-cp39-win_amd64.whl", hash = "sha256:45c08bec71d3546d606989ba6e7daa6f0992918171e2a6f7fbedfa7361c2de1e"},
|
||||
{file = "scipy-1.13.0.tar.gz", hash = "sha256:58569af537ea29d3f78e5abd18398459f195546bb3be23d16677fb26616cc11e"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = ">=1.22.4,<1.29.0"
|
||||
numpy = ">=1.22.4,<2.3"
|
||||
|
||||
[package.extras]
|
||||
dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
|
||||
doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
|
||||
test = ["asv", "gmpy2", "hypothesis", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
|
||||
dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
|
||||
doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.12.0)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0)", "sphinx-design (>=0.4.0)"]
|
||||
test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
|
||||
|
||||
[[package]]
|
||||
name = "sentry-sdk"
|
||||
version = "1.43.0"
|
||||
version = "1.44.1"
|
||||
description = "Python client for Sentry (https://sentry.io)"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{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"},
|
||||
{file = "sentry-sdk-1.44.1.tar.gz", hash = "sha256:24e6a53eeabffd2f95d952aa35ca52f0f4201d17f820ac9d3ff7244c665aaf68"},
|
||||
{file = "sentry_sdk-1.44.1-py2.py3-none-any.whl", hash = "sha256:5f75eb91d8ab6037c754a87b8501cc581b2827e923682f593bed3539ce5b3999"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -3106,7 +3186,7 @@ testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jar
|
|||
name = "shapely"
|
||||
version = "2.0.3"
|
||||
description = "Manipulation and analysis of geometric objects"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "shapely-2.0.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:af7e9abe180b189431b0f490638281b43b84a33a960620e6b2e8d3e3458b61a1"},
|
||||
|
|
@ -3273,7 +3353,7 @@ tests = ["pytest", "pytest-cov"]
|
|||
name = "tifffile"
|
||||
version = "2024.2.12"
|
||||
description = "Read and write TIFF files"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "tifffile-2024.2.12-py3-none-any.whl", hash = "sha256:870998f82fbc94ff7c3528884c1b0ae54863504ff51dbebea431ac3fa8fb7c21"},
|
||||
|
|
@ -3299,36 +3379,36 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "torch"
|
||||
version = "2.2.1"
|
||||
version = "2.2.2"
|
||||
description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
|
||||
optional = false
|
||||
python-versions = ">=3.8.0"
|
||||
files = [
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[package.dependencies]
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||||
|
|
@ -3357,42 +3437,42 @@ optree = ["optree (>=0.9.1)"]
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|||
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[[package]]
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optional = false
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|
||||
{file = "torchvision-0.17.2-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:32dbeba3987e20f2dc1bce8d1504139fff582898346dfe8ad98d649f97ca78fa"},
|
||||
{file = "torchvision-0.17.2-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:35ba5c1600c3203549d2316422a659bd20c0cfda1b6085eec94fb9f35f55ca43"},
|
||||
{file = "torchvision-0.17.2-cp38-cp38-win_amd64.whl", hash = "sha256:2f69570f50b1d195e51bc03feffb7b7728207bc36efcfb1f0813712b2379d881"},
|
||||
{file = "torchvision-0.17.2-cp39-cp39-macosx_10_13_x86_64.whl", hash = "sha256:4868bbfa55758c8107e69a0e7dd5e77b89056035cd38b767ad5b98cdb71c0f0d"},
|
||||
{file = "torchvision-0.17.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:efd6d0dd0668e15d01a2cffadc74068433b32cbcf5692e0c4aa15fc5cb250ce7"},
|
||||
{file = "torchvision-0.17.2-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:7dc85b397f6c6d9ef12716ce0d6e11ac2b803f5cccff6fe3966db248e7774478"},
|
||||
{file = "torchvision-0.17.2-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:d506854c5acd69b20a8b6641f01fe841685a21c5406b56813184f1c9fc94279e"},
|
||||
{file = "torchvision-0.17.2-cp39-cp39-win_amd64.whl", hash = "sha256:067095e87a020a7a251ac1d38483aa591c5ccb81e815527c54db88a982fc9267"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = "*"
|
||||
pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
|
||||
torch = "2.2.1"
|
||||
torch = "2.2.2"
|
||||
|
||||
[package.extras]
|
||||
scipy = ["scipy"]
|
||||
|
|
@ -3442,13 +3522,13 @@ tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
|
|||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.10.0"
|
||||
version = "4.11.0"
|
||||
description = "Backported and Experimental Type Hints for Python 3.8+"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
|
||||
{file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
|
||||
{file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
|
||||
{file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -3501,13 +3581,13 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess
|
|||
|
||||
[[package]]
|
||||
name = "wandb"
|
||||
version = "0.16.4"
|
||||
version = "0.16.6"
|
||||
description = "A CLI and library for interacting with the Weights & Biases API."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "wandb-0.16.4-py3-none-any.whl", hash = "sha256:bb9eb5aa2c2c85e11c76040c4271366f54d4975167aa6320ba86c3f2d97fe5fa"},
|
||||
{file = "wandb-0.16.4.tar.gz", hash = "sha256:8752c67d1347a4c29777e64dc1e1a742a66c5ecde03aebadf2b0d62183fa307c"},
|
||||
{file = "wandb-0.16.6-py3-none-any.whl", hash = "sha256:5810019a3b981c796e98ea58557a7c380f18834e0c6bdaed15df115522e5616e"},
|
||||
{file = "wandb-0.16.6.tar.gz", hash = "sha256:86f491e3012d715e0d7d7421a4d6de41abef643b7403046261f962f3e512fe1c"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -3539,13 +3619,13 @@ sweeps = ["sweeps (>=0.2.0)"]
|
|||
|
||||
[[package]]
|
||||
name = "werkzeug"
|
||||
version = "3.0.1"
|
||||
version = "3.0.2"
|
||||
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"},
|
||||
{file = "werkzeug-3.0.2-py3-none-any.whl", hash = "sha256:3aac3f5da756f93030740bc235d3e09449efcf65f2f55e3602e1d851b8f48795"},
|
||||
{file = "werkzeug-3.0.2.tar.gz", hash = "sha256:e39b645a6ac92822588e7b39a692e7828724ceae0b0d702ef96701f90e70128d"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -3556,20 +3636,20 @@ watchdog = ["watchdog (>=2.3)"]
|
|||
|
||||
[[package]]
|
||||
name = "zarr"
|
||||
version = "2.17.1"
|
||||
version = "2.17.2"
|
||||
description = "An implementation of chunked, compressed, N-dimensional arrays for Python"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "zarr-2.17.1-py3-none-any.whl", hash = "sha256:e25df2741a6e92645f3890f30f3136d5b57a0f8f831094b024bbcab5f2797bc7"},
|
||||
{file = "zarr-2.17.1.tar.gz", hash = "sha256:564b3aa072122546fe69a0fa21736f466b20fad41754334b62619f088ce46261"},
|
||||
{file = "zarr-2.17.2-py3-none-any.whl", hash = "sha256:70d7cc07c24280c380ef80644151d136b7503b0d83c9f214e8000ddc0f57f69b"},
|
||||
{file = "zarr-2.17.2.tar.gz", hash = "sha256:2cbaa6cb4e342d45152d4a7a4b2013c337fcd3a8e7bc98253560180de60552ce"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
asciitree = "*"
|
||||
fasteners = {version = "*", markers = "sys_platform != \"emscripten\""}
|
||||
numcodecs = ">=0.10.0"
|
||||
numpy = ">=1.21.1"
|
||||
numpy = ">=1.23"
|
||||
|
||||
[package.extras]
|
||||
docs = ["numcodecs[msgpack]", "numpydoc", "pydata-sphinx-theme", "sphinx", "sphinx-automodapi", "sphinx-copybutton", "sphinx-design", "sphinx-issues"]
|
||||
|
|
@ -3598,4 +3678,4 @@ xarm = ["gym-xarm"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.10"
|
||||
content-hash = "bf4627c62a45764931729ce373f1038fe289b6caebb01e66d878f6f278c54518"
|
||||
content-hash = "7ec0310f8dd0ffa4d92fa78e06513bce98c3657692b3753ff34aadd297a3766c"
|
||||
|
|
|
|||
|
|
@ -23,7 +23,6 @@ packages = [{include = "lerobot"}]
|
|||
python = "^3.10"
|
||||
termcolor = "^2.4.0"
|
||||
omegaconf = "^2.3.0"
|
||||
dm-env = "^1.6"
|
||||
pandas = "^2.2.1"
|
||||
wandb = "^0.16.3"
|
||||
moviepy = "^1.0.3"
|
||||
|
|
@ -34,20 +33,15 @@ einops = "^0.7.0"
|
|||
pygame = "^2.5.2"
|
||||
pymunk = "^6.6.0"
|
||||
zarr = "^2.17.0"
|
||||
shapely = "^2.0.3"
|
||||
scikit-image = "^0.22.0"
|
||||
numba = "^0.59.0"
|
||||
mpmath = "^1.3.0"
|
||||
torch = "^2.2.1"
|
||||
mujoco = "^2.3.7"
|
||||
opencv-python = "^4.9.0.80"
|
||||
diffusers = "^0.26.3"
|
||||
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"
|
||||
gymnasium-robotics = "^1.2.4"
|
||||
gymnasium = "^0.29.1"
|
||||
cmake = "^3.29.0.1"
|
||||
gym-pusht = { git = "git@github.com:huggingface/gym-pusht.git", optional = true}
|
||||
|
|
@ -62,9 +56,17 @@ pusht = ["gym-pusht"]
|
|||
xarm = ["gym-xarm"]
|
||||
aloha = ["gym-aloha"]
|
||||
|
||||
|
||||
[tool.poetry.group.dev]
|
||||
optional = true
|
||||
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
pre-commit = "^3.6.2"
|
||||
debugpy = "^1.8.1"
|
||||
|
||||
|
||||
[tool.poetry.group.test.dependencies]
|
||||
pytest = "^8.1.0"
|
||||
pytest-cov = "^5.0.0"
|
||||
|
||||
|
|
@ -108,3 +110,6 @@ enable = true
|
|||
[build-system]
|
||||
requires = ["poetry-core>=1.0.0", "poetry-dynamic-versioning>=1.0.0,<2.0.0"]
|
||||
build-backend = "poetry_dynamic_versioning.backend"
|
||||
|
||||
[tool.black]
|
||||
line-length = 110
|
||||
|
|
|
|||
|
|
@ -33,8 +33,8 @@ def test_env(env_name, task, obs_type):
|
|||
package_name = f"gym_{env_name}"
|
||||
importlib.import_module(package_name)
|
||||
env = gym.make(f"{package_name}/{task}", obs_type=obs_type)
|
||||
check_env(env.unwrapped)
|
||||
|
||||
check_env(env.unwrapped, skip_render_check=True)
|
||||
env.close()
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"env_name",
|
||||
|
|
@ -61,3 +61,5 @@ def test_factory(env_name):
|
|||
# TODO(rcadene): we assume for now that image normalization takes place in the model
|
||||
assert img.max() <= 1.0
|
||||
assert img.min() >= 0.0
|
||||
|
||||
env.close()
|
||||
|
|
|
|||
Loading…
Reference in New Issue