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lerobot
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Merge branch 'main' into local_logging_tensorboard

This commit is contained in:
j 2024-05-13 10:55:45 -04:00
parent 449d44c1fb 89c6be84ca
commit ccf2782d8a
30 changed files with 430 additions and 278 deletions
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32
.github/workflows/test.yml vendored
View File

@ -57,6 +57,38 @@ jobs:
&& rm -rf tests/outputs outputs
pytest-minimal:
name: Pytest (minimal install)
runs-on: ubuntu-latest
env:
DATA_DIR: tests/data
MUJOCO_GL: egl
steps:
- uses: actions/checkout@v4
- name: Install poetry
run: |
pipx install poetry && poetry config virtualenvs.in-project true
echo "${{ github.workspace }}/.venv/bin" >> $GITHUB_PATH
- name: Set up Python 3.10
uses: actions/setup-python@v5
with:
python-version: "3.10"
- name: Install poetry dependencies
run: |
poetry install --extras "test"
- name: Test with pytest
run: |
pytest tests -v --cov=./lerobot --durations=0 \
-W ignore::DeprecationWarning:imageio_ffmpeg._utils:7 \
-W ignore::UserWarning:torch.utils.data.dataloader:558 \
-W ignore::UserWarning:gymnasium.utils.env_checker:247 \
&& rm -rf tests/outputs outputs
end-to-end:
name: End-to-end
runs-on: ubuntu-latest

7
Makefile
View File

@ -22,9 +22,8 @@ test-end-to-end:
${MAKE} test-act-ete-eval
${MAKE} test-diffusion-ete-train
${MAKE} test-diffusion-ete-eval
# TODO(rcadene, alexander-soare): enable end-to-end tests for tdmpc
# ${MAKE} test-tdmpc-ete-train
# ${MAKE} test-tdmpc-ete-eval
${MAKE} test-tdmpc-ete-train
${MAKE} test-tdmpc-ete-eval
${MAKE} test-default-ete-eval
test-act-ete-train:
@ -80,7 +79,7 @@ test-tdmpc-ete-train:
policy=tdmpc \
env=xarm \
env.task=XarmLift-v0 \
dataset_repo_id=lerobot/xarm_lift_medium_replay \
dataset_repo_id=lerobot/xarm_lift_medium \
wandb.enable=False \
training.offline_steps=2 \
training.online_steps=2 \

1
README.md
View File

@ -57,7 +57,6 @@
- Thanks to Tony Zaho, Zipeng Fu and colleagues for open sourcing ACT policy, ALOHA environments and datasets. Ours are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha) and [Mobile ALOHA](https://mobile-aloha.github.io).
- Thanks to Cheng Chi, Zhenjia Xu and colleagues for open sourcing Diffusion policy, Pusht environment and datasets, as well as UMI datasets. Ours are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu) and [UMI Gripper](https://umi-gripper.github.io).
- Thanks to Nicklas Hansen, Yunhai Feng and colleagues for open sourcing TDMPC policy, Simxarm environments and datasets. Ours are adapted from [TDMPC](https://github.com/nicklashansen/tdmpc) and [FOWM](https://www.yunhaifeng.com/FOWM).
- Thanks to Vincent Moens and colleagues for open sourcing [TorchRL](https://github.com/pytorch/rl). It allowed for quick experimentations on the design of `LeRobot`.
- Thanks to Antonio Loquercio and Ashish Kumar for their early support.

8
docker/lerobot-gpu/Dockerfile
View File

@ -7,6 +7,11 @@ ARG DEBIAN_FRONTEND=noninteractive
# Install apt dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential cmake \
git git-lfs openssh-client \
nano vim \
htop atop nvtop \
sed gawk grep curl wget \
tcpdump sysstat screen \
libglib2.0-0 libgl1-mesa-glx libegl1-mesa \
python${PYTHON_VERSION} python${PYTHON_VERSION}-venv \
&& apt-get clean && rm -rf /var/lib/apt/lists/*
@ -18,7 +23,8 @@ ENV PATH="/opt/venv/bin:$PATH"
RUN echo "source /opt/venv/bin/activate" >> /root/.bashrc
# Install LeRobot
COPY . /lerobot
RUN git lfs install
RUN git clone https://github.com/huggingface/lerobot.git
WORKDIR /lerobot
RUN pip install --upgrade --no-cache-dir pip
RUN pip install --no-cache-dir ".[test, aloha, xarm, pusht]"

14
lerobot/common/logger.py
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@ -88,9 +88,9 @@ class Logger:
# Also save the full Hydra config for the env configuration.
OmegaConf.save(self._cfg, save_dir / "config.yaml")
if self._wandb and not self._disable_wandb_artifact:
# note wandb artifact does not accept ":" in its name
# note wandb artifact does not accept ":" or "/" in its name
artifact = self._wandb.Artifact(
self._group.replace(":", "_") + "-" + str(self._seed) + "-" + str(identifier),
f"{self._group.replace(':', '_').replace('/', '_')}-{self._seed}-{identifier}",
type="model",
)
artifact.add_file(save_dir / SAFETENSORS_SINGLE_FILE)
@ -100,9 +100,10 @@ class Logger:
self._buffer_dir.mkdir(parents=True, exist_ok=True)
fp = self._buffer_dir / f"{str(identifier)}.pkl"
buffer.save(fp)
if self._wandb:
if self._wandb and not self._disable_wandb_artifact:
# note wandb artifact does not accept ":" or "/" in its name
artifact = self._wandb.Artifact(
self._group + "-" + str(self._seed) + "-" + str(identifier),
f"{self._group.replace(':', '_').replace('/', '_')}-{self._seed}-{identifier}",
type="buffer",
)
artifact.add_file(fp)
@ -120,6 +121,11 @@ class Logger:
assert mode in {"train", "eval"}
if self._wandb is not None:
for k, v in d.items():
if not isinstance(v, (int, float, str)):
logging.warning(
f'WandB logging of key "{k}" was ignored as its type is not handled by this wrapper.'
)
continue
self._wandb.log({f"{mode}/{k}": v}, step=step)
elif self._local_writer is not None:
for k, v in d.items():

4
lerobot/common/policies/act/modeling_act.py
View File

@ -101,7 +101,7 @@ class ACTPolicy(nn.Module, PyTorchModelHubMixin):
F.l1_loss(batch["action"], actions_hat, reduction="none") * ~batch["action_is_pad"].unsqueeze(-1)
).mean()
loss_dict = {"l1_loss": l1_loss}
loss_dict = {"l1_loss": l1_loss.item()}
if self.config.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
@ -110,7 +110,7 @@ class ACTPolicy(nn.Module, PyTorchModelHubMixin):
mean_kld = (
(-0.5 * (1 + log_sigma_x2_hat - mu_hat.pow(2) - (log_sigma_x2_hat).exp())).sum(-1).mean()
)
loss_dict["kld_loss"] = mean_kld
loss_dict["kld_loss"] = mean_kld.item()
loss_dict["loss"] = l1_loss + mean_kld * self.config.kl_weight
else:
loss_dict["loss"] = l1_loss

8
lerobot/common/policies/diffusion/configuration_diffusion.py
View File

@ -51,6 +51,7 @@ class DiffusionConfig:
use_film_scale_modulation: FiLM (https://arxiv.org/abs/1709.07871) is used for the Unet conditioning.
Bias modulation is used be default, while this parameter indicates whether to also use scale
modulation.
noise_scheduler_type: Name of the noise scheduler to use. Supported options: ["DDPM", "DDIM"].
num_train_timesteps: Number of diffusion steps for the forward diffusion schedule.
beta_schedule: Name of the diffusion beta schedule as per DDPMScheduler from Hugging Face diffusers.
beta_start: Beta value for the first forward-diffusion step.
@ -110,6 +111,7 @@ class DiffusionConfig:
diffusion_step_embed_dim: int = 128
use_film_scale_modulation: bool = True
# Noise scheduler.
noise_scheduler_type: str = "DDPM"
num_train_timesteps: int = 100
beta_schedule: str = "squaredcos_cap_v2"
beta_start: float = 0.0001
@ -144,3 +146,9 @@ class DiffusionConfig:
raise ValueError(
f"`prediction_type` must be one of {supported_prediction_types}. Got {self.prediction_type}."
)
supported_noise_schedulers = ["DDPM", "DDIM"]
if self.noise_scheduler_type not in supported_noise_schedulers:
raise ValueError(
f"`noise_scheduler_type` must be one of {supported_noise_schedulers}. "
f"Got {self.noise_scheduler_type}."
)

28
lerobot/common/policies/diffusion/modeling_diffusion.py
View File

@ -13,6 +13,7 @@ import einops
import torch
import torch.nn.functional as F # noqa: N812
import torchvision
from diffusers.schedulers.scheduling_ddim import DDIMScheduler
from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
from huggingface_hub import PyTorchModelHubMixin
from robomimic.models.base_nets import SpatialSoftmax
@ -126,6 +127,19 @@ class DiffusionPolicy(nn.Module, PyTorchModelHubMixin):
return {"loss": loss}
def _make_noise_scheduler(name: str, **kwargs: dict) -> DDPMScheduler | DDIMScheduler:
"""
Factory for noise scheduler instances of the requested type. All kwargs are passed
to the scheduler.
"""
if name == "DDPM":
return DDPMScheduler(**kwargs)
elif name == "DDIM":
return DDIMScheduler(**kwargs)
else:
raise ValueError(f"Unsupported noise scheduler type {name}")
class DiffusionModel(nn.Module):
def __init__(self, config: DiffusionConfig):
super().__init__()
@ -138,12 +152,12 @@ class DiffusionModel(nn.Module):
* config.n_obs_steps,
)
self.noise_scheduler = DDPMScheduler(
self.noise_scheduler = _make_noise_scheduler(
config.noise_scheduler_type,
num_train_timesteps=config.num_train_timesteps,
beta_start=config.beta_start,
beta_end=config.beta_end,
beta_schedule=config.beta_schedule,
variance_type="fixed_small",
clip_sample=config.clip_sample,
clip_sample_range=config.clip_sample_range,
prediction_type=config.prediction_type,
@ -315,11 +329,13 @@ class DiffusionRgbEncoder(nn.Module):
# Set up pooling and final layers.
# Use a dry run to get the feature map shape.
# The dummy input should take the number of image channels from `config.input_shapes` and it should use the
# height and width from `config.crop_shape`.
dummy_input = torch.zeros(size=(1, config.input_shapes["observation.image"][0], *config.crop_shape))
with torch.inference_mode():
feat_map_shape = tuple(
self.backbone(torch.zeros(size=(1, *config.input_shapes["observation.image"]))).shape[1:]
)
self.pool = SpatialSoftmax(feat_map_shape, num_kp=config.spatial_softmax_num_keypoints)
dummy_feature_map = self.backbone(dummy_input)
feature_map_shape = tuple(dummy_feature_map.shape[1:])
self.pool = SpatialSoftmax(feature_map_shape, num_kp=config.spatial_softmax_num_keypoints)
self.feature_dim = config.spatial_softmax_num_keypoints * 2
self.out = nn.Linear(config.spatial_softmax_num_keypoints * 2, self.feature_dim)
self.relu = nn.ReLU()

3
lerobot/common/policies/policy_protocol.py
View File

@ -38,7 +38,8 @@ class Policy(Protocol):
def forward(self, batch: dict[str, Tensor]) -> dict:
"""Run the batch through the model and compute the loss for training or validation.
Returns a dictionary with "loss" and maybe other information.
Returns a dictionary with "loss" and potentially other information. Apart from "loss" which is a Tensor, all
other items should be logging-friendly, native Python types.
"""
def select_action(self, batch: dict[str, Tensor]):

2
lerobot/common/policies/tdmpc/configuration_tdmpc.py
View File

@ -47,7 +47,7 @@ class TDMPCConfig:
elite_weighting_temperature: The temperature to use for softmax weighting (by trajectory value) of the
elites, when updating the gaussian parameters for CEM.
gaussian_mean_momentum: Momentum (α) used for EMA updates of the mean parameter μ of the gaussian
paramters optimized in CEM. Updates are calculated as μ⁻ ← αμ⁻ + (1-α)μ.
parameters optimized in CEM. Updates are calculated as μ⁻ ← αμ⁻ + (1-α)μ.
max_random_shift_ratio: Maximum random shift (as a proportion of the image size) to apply to the
image(s) (in units of pixels) for training-time augmentation. If set to 0, no such augmentation
is applied. Note that the input images are assumed to be square for this augmentation.

27
lerobot/common/utils/utils.py
View File

@ -1,8 +1,10 @@
import logging
import os.path as osp
import random
from contextlib import contextmanager
from datetime import datetime
from pathlib import Path
from typing import Generator
import hydra
import numpy as np
@ -39,6 +41,31 @@ def set_global_seed(seed):
torch.cuda.manual_seed_all(seed)
@contextmanager
def seeded_context(seed: int) -> Generator[None, None, None]:
"""Set the seed when entering a context, and restore the prior random state at exit.
Example usage:
```
a = random.random() # produces some random number
with seeded_context(1337):
b = random.random() # produces some other random number
c = random.random() # produces yet another random number, but the same it would have if we never made `b`
```
"""
random_state = random.getstate()
np_random_state = np.random.get_state()
torch_random_state = torch.random.get_rng_state()
torch_cuda_random_state = torch.cuda.random.get_rng_state()
set_global_seed(seed)
yield None
random.setstate(random_state)
np.random.set_state(np_random_state)
torch.random.set_rng_state(torch_random_state)
torch.cuda.random.set_rng_state(torch_cuda_random_state)
def init_logging():
def custom_format(record):
dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

12
lerobot/configs/policy/act.yaml
View File

@ -3,6 +3,12 @@
seed: 1000
dataset_repo_id: lerobot/aloha_sim_insertion_human
override_dataset_stats:
observation.images.top:
# stats from imagenet, since we use a pretrained vision model
mean: [[[0.485]], [[0.456]], [[0.406]]] # (c,1,1)
std: [[[0.229]], [[0.224]], [[0.225]]] # (c,1,1)
training:
offline_steps: 80000
online_steps: 0
@ -18,12 +24,6 @@ training:
grad_clip_norm: 10
online_steps_between_rollouts: 1
override_dataset_stats:
observation.images.top:
# stats from imagenet, since we use a pretrained vision model
mean: [[[0.485]], [[0.456]], [[0.406]]] # (c,1,1)
std: [[[0.229]], [[0.224]], [[0.225]]] # (c,1,1)
delta_timestamps:
action: "[i / ${fps} for i in range(${policy.chunk_size})]"

29
lerobot/configs/policy/diffusion.yaml
View File

@ -7,6 +7,20 @@
seed: 100000
dataset_repo_id: lerobot/pusht
override_dataset_stats:
# TODO(rcadene, alexander-soare): should we remove image stats as well? do we use a pretrained vision model?
observation.image:
mean: [[[0.5]], [[0.5]], [[0.5]]] # (c,1,1)
std: [[[0.5]], [[0.5]], [[0.5]]] # (c,1,1)
# TODO(rcadene, alexander-soare): we override state and action stats to use the same as the pretrained model
# from the original codebase, but we should remove these and train our own pretrained model
observation.state:
min: [13.456424, 32.938293]
max: [496.14618, 510.9579]
action:
min: [12.0, 25.0]
max: [511.0, 511.0]
training:
offline_steps: 200000
online_steps: 0
@ -34,20 +48,6 @@ eval:
n_episodes: 50
batch_size: 50
override_dataset_stats:
# TODO(rcadene, alexander-soare): should we remove image stats as well? do we use a pretrained vision model?
observation.image:
mean: [[[0.5]], [[0.5]], [[0.5]]] # (c,1,1)
std: [[[0.5]], [[0.5]], [[0.5]]] # (c,1,1)
# TODO(rcadene, alexander-soare): we override state and action stats to use the same as the pretrained model
# from the original codebase, but we should remove these and train our own pretrained model
observation.state:
min: [13.456424, 32.938293]
max: [496.14618, 510.9579]
action:
min: [12.0, 25.0]
max: [511.0, 511.0]
policy:
name: diffusion
@ -85,6 +85,7 @@ policy:
diffusion_step_embed_dim: 128
use_film_scale_modulation: True
# Noise scheduler.
noise_scheduler_type: DDPM
num_train_timesteps: 100
beta_schedule: squaredcos_cap_v2
beta_start: 0.0001

2
lerobot/configs/policy/tdmpc.yaml
View File

@ -1,7 +1,7 @@
# @package _global_
seed: 1
dataset_repo_id: lerobot/xarm_lift_medium_replay
dataset_repo_id: lerobot/xarm_lift_medium
training:
offline_steps: 25000

4
lerobot/scripts/train.py
View File

@ -72,6 +72,7 @@ def make_optimizer_and_scheduler(cfg, policy):
def update_policy(policy, batch, optimizer, grad_clip_norm, lr_scheduler=None):
"""Returns a dictionary of items for logging."""
start_time = time.time()
policy.train()
output_dict = policy.forward(batch)
@ -99,6 +100,7 @@ def update_policy(policy, batch, optimizer, grad_clip_norm, lr_scheduler=None):
"grad_norm": float(grad_norm),
"lr": optimizer.param_groups[0]["lr"],
"update_s": time.time() - start_time,
**{k: v for k, v in output_dict.items() if k != "loss"},
}
return info
@ -122,7 +124,7 @@ def train_notebook(out_dir=None, job_name=None, config_name="default", config_pa
train(cfg, out_dir=out_dir, job_name=job_name)
def log_train_info(logger, info, step, cfg, dataset, is_offline):
def log_train_info(logger: Logger, info, step, cfg, dataset, is_offline):
loss = info["loss"]
grad_norm = info["grad_norm"]
lr = info["lr"]

13
lerobot/scripts/visualize_dataset.py
View File

@ -47,6 +47,7 @@ local$ rerun ws://localhost:9087
"""
import argparse
import gc
import logging
import time
from pathlib import Path
@ -115,15 +116,17 @@ def visualize_dataset(
spawn_local_viewer = mode == "local" and not save
rr.init(f"{repo_id}/episode_{episode_index}", spawn=spawn_local_viewer)
# Manually call python garbage collector after `rr.init` to avoid hanging in a blocking flush
# when iterating on a dataloader with `num_workers` > 0
# TODO(rcadene): remove `gc.collect` when rerun version 0.16 is out, which includes a fix
gc.collect()
if mode == "distant":
rr.serve(open_browser=False, web_port=web_port, ws_port=ws_port)
logging.info("Logging to Rerun")
if num_workers > 0:
# TODO(rcadene): fix data workers hanging when `rr.init` is called
logging.warning("If data loader is hanging, try `--num-workers 0`.")
for batch in tqdm.tqdm(dataloader, total=len(dataloader)):
# iterate over the batch
for i in range(len(batch["index"])):
@ -196,7 +199,7 @@ def main():
parser.add_argument(
"--num-workers",
type=int,
default=0,
default=4,
help="Number of processes of Dataloader for loading the data.",
)
parser.add_argument(

396
poetry.lock generated
View File

@ -131,17 +131,6 @@ files = [
{file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"},
]
[[package]]
name = "appdirs"
version = "1.4.4"
description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
optional = false
python-versions = "*"
files = [
{file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"},
{file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"},
]
[[package]]
name = "asciitree"
version = "0.3.3"
@ -1108,67 +1097,67 @@ protobuf = ["grpcio-tools (>=1.63.0)"]
[[package]]
name = "gym-aloha"
version = "0.1.0"
version = "0.1.1"
description = "A gym environment for ALOHA"
optional = true
python-versions = "<4.0,>=3.10"
files = [
{file = "gym_aloha-0.1.0-py3-none-any.whl", hash = "sha256:62e36eeb09284422cbb7baca0292c6f65e38ec8774bf9b0bf7159ad5990cf29a"},
{file = "gym_aloha-0.1.0.tar.gz", hash = "sha256:bab332f469ba5ffe655fc3e9647aead05d2cb3b950dfb1f299b9539b3857ad7e"},
{file = "gym_aloha-0.1.1-py3-none-any.whl", hash = "sha256:2698037246dbb106828f0bc229b61007b0a21d5967c72cc373f7bc1083203584"},
{file = "gym_aloha-0.1.1.tar.gz", hash = "sha256:614ae1cf116323e7b5ae2f0e9bd282c4f052aee15e839e5587ddce45995359bc"},
]
[package.dependencies]
dm-control = "1.0.14"
gymnasium = ">=0.29.1,<0.30.0"
imageio = {version = ">=2.34.0,<3.0.0", extras = ["ffmpeg"]}
dm-control = ">=1.0.14"
gymnasium = ">=0.29.1"
imageio = {version = ">=2.34.0", extras = ["ffmpeg"]}
mujoco = ">=2.3.7,<3.0.0"
[package.extras]
dev = ["debugpy (>=1.8.1,<2.0.0)", "pre-commit (>=3.7.0,<4.0.0)"]
test = ["pytest (>=8.1.0,<9.0.0)", "pytest-cov (>=5.0.0,<6.0.0)"]
dev = ["debugpy (>=1.8.1)", "pre-commit (>=3.7.0)"]
test = ["pytest (>=8.1.0)", "pytest-cov (>=5.0.0)"]
[[package]]
name = "gym-pusht"
version = "0.1.1"
version = "0.1.3"
description = "A gymnasium environment for PushT."
optional = true
python-versions = "<4.0,>=3.10"
files = [
{file = "gym_pusht-0.1.1-py3-none-any.whl", hash = "sha256:dcf8644713db48286e907aabb11e005b0592632e323baa40d1a4f2dfbbc76c3d"},
{file = "gym_pusht-0.1.1.tar.gz", hash = "sha256:0d1c9ffd4ad0e2411efcc724003a365a853f20b6d596980c113e7ec181ac021f"},
{file = "gym_pusht-0.1.3-py3-none-any.whl", hash = "sha256:feeb02493a03d1aacc45d43d6397962c50ed779ab7e4019d73af11d2f0b3831b"},
{file = "gym_pusht-0.1.3.tar.gz", hash = "sha256:c8e9a5256035ba49841ebbc7c32a06c4fa2daa52f5fad80da941b607c4553e28"},
]
[package.dependencies]
gymnasium = ">=0.29.1,<0.30.0"
opencv-python = ">=4.9.0.80,<5.0.0.0"
pygame = ">=2.5.2,<3.0.0"
pymunk = ">=6.6.0,<7.0.0"
gymnasium = ">=0.29.1"
opencv-python = ">=4.9.0"
pygame = ">=2.5.2"
pymunk = ">=6.6.0"
scikit-image = ">=0.22.0"
shapely = ">=2.0.3,<3.0.0"
shapely = ">=2.0.3"
[package.extras]
dev = ["debugpy (>=1.8.1,<2.0.0)", "pre-commit (>=3.7.0,<4.0.0)"]
test = ["pytest (>=8.1.0,<9.0.0)", "pytest-cov (>=5.0.0,<6.0.0)"]
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]
[[package]]
@ -3813,13 +3803,13 @@ tests = ["pytest", "pytest-cov"]
[[package]]
name = "tifffile"
version = "2024.5.3"
version = "2024.5.10"
description = "Read and write TIFF files"
optional = true
python-versions = ">=3.9"
files = [
{file = "tifffile-2024.5.3-py3-none-any.whl", hash = "sha256:cac4d939156ff7f16d65fd689637808a7b5b3ad58f9c73327fc009b0aa32c7d5"},
{file = "tifffile-2024.5.3.tar.gz", hash = "sha256:44521508ecc51ebaf0e47e9748913e9c7331a4e32fb571ff4dfc05cb8f4d8896"},
{file = "tifffile-2024.5.10-py3-none-any.whl", hash = "sha256:4154f091aa24d4e75bfad9ab2d5424a68c70e67b8220188066dc61946d4551bd"},
{file = "tifffile-2024.5.10.tar.gz", hash = "sha256:aa1e1b12be952ab20717d6848bd6d4a5ee88d2aa319f1152bff4354ad728ec86"},
]
[package.dependencies]
@ -4034,36 +4024,40 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess
[[package]]
name = "wandb"
version = "0.16.6"
version = "0.17.0"
description = "A CLI and library for interacting with the Weights & Biases API."
optional = false
python-versions = ">=3.7"
files = [
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{file = "wandb-0.16.6.tar.gz", hash = "sha256:86f491e3012d715e0d7d7421a4d6de41abef643b7403046261f962f3e512fe1c"},
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]
[package.dependencies]
appdirs = ">=1.4.3"
Click = ">=7.1,<8.0.0 || >8.0.0"
click = ">=7.1,<8.0.0 || >8.0.0"
docker-pycreds = ">=0.4.0"
GitPython = ">=1.0.0,<3.1.29 || >3.1.29"
gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
platformdirs = "*"
protobuf = {version = ">=3.19.0,<4.21.0 || >4.21.0,<5", markers = "python_version > \"3.9\" or sys_platform != \"linux\""}
psutil = ">=5.0.0"
PyYAML = "*"
pyyaml = "*"
requests = ">=2.0.0,<3"
sentry-sdk = ">=1.0.0"
setproctitle = "*"
setuptools = "*"
[package.extras]
async = ["httpx (>=0.23.0)"]
aws = ["boto3"]
azure = ["azure-identity", "azure-storage-blob"]
gcp = ["google-cloud-storage"]
importers = ["filelock", "mlflow", "polars", "rich", "tenacity"]
kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
launch = ["PyYAML (>=6.0.0)", "awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "tomli", "typing-extensions"]
launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "typing-extensions"]
media = ["bokeh", "moviepy", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit-pypi", "soundfile"]
models = ["cloudpickle"]
perf = ["orjson"]
@ -4309,13 +4303,13 @@ multidict = ">=4.0"
[[package]]
name = "zarr"
version = "2.17.2"
version = "2.18.0"
description = "An implementation of chunked, compressed, N-dimensional arrays for Python"
optional = false
python-versions = ">=3.9"
files = [
{file = "zarr-2.17.2-py3-none-any.whl", hash = "sha256:70d7cc07c24280c380ef80644151d136b7503b0d83c9f214e8000ddc0f57f69b"},
{file = "zarr-2.17.2.tar.gz", hash = "sha256:2cbaa6cb4e342d45152d4a7a4b2013c337fcd3a8e7bc98253560180de60552ce"},
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{file = "zarr-2.18.0.tar.gz", hash = "sha256:c3b7d2c85b8a42b0ad0ad268a36fb6886ca852098358c125c6b126a417e0a598"},
]
[package.dependencies]
@ -4354,4 +4348,4 @@ xarm = ["gym-xarm"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.10,<3.13"
content-hash = "008a6af5ad9d9eafbd933c922c2c5d84fddae85aff8a9eefc0538b1319966f6e"
content-hash = "2f0d2cbf4a2dec546e25b29b9b108ff1f97b4c278b718360b3f7f6a2bf9dcef8"

56
pyproject.toml
View File

@ -28,37 +28,37 @@ packages = [{include = "lerobot"}]
[tool.poetry.dependencies]
python = ">=3.10,<3.13"
termcolor = "^2.4.0"
omegaconf = "^2.3.0"
wandb = "^0.16.3"
imageio = {extras = ["ffmpeg"], version = "^2.34.0"}
gdown = "^5.1.0"
hydra-core = "^1.3.2"
einops = "^0.8.0"
pymunk = "^6.6.0"
zarr = "^2.17.0"
numba = "^0.59.0"
termcolor = ">=2.4.0"
omegaconf = ">=2.3.0"
wandb = ">=0.16.3"
imageio = {extras = ["ffmpeg"], version = ">=2.34.0"}
gdown = ">=5.1.0"
hydra-core = ">=1.3.2"
einops = ">=0.8.0"
pymunk = ">=6.6.0"
zarr = ">=2.17.0"
numba = ">=0.59.0"
torch = "^2.2.1"
opencv-python = "^4.9.0.80"
opencv-python = ">=4.9.0"
diffusers = "^0.27.2"
torchvision = "^0.18.0"
h5py = "^3.10.0"
huggingface-hub = "^0.21.4"
torchvision = ">=0.18.0"
h5py = ">=3.10.0"
huggingface-hub = ">=0.21.4"
robomimic = "0.2.0"
gymnasium = "^0.29.1"
cmake = "^3.29.0.1"
gym-pusht = { version = "^0.1.1", optional = true}
gym-xarm = { version = "^0.1.0", optional = true}
gym-aloha = { version = "^0.1.0", optional = true}
pre-commit = {version = "^3.7.0", optional = true}
debugpy = {version = "^1.8.1", optional = true}
pytest = {version = "^8.1.0", optional = true}
pytest-cov = {version = "^5.0.0", optional = true}
gymnasium = ">=0.29.1"
cmake = ">=3.29.0.1"
gym-pusht = { version = ">=0.1.3", optional = true}
gym-xarm = { version = ">=0.1.1", optional = true}
gym-aloha = { version = ">=0.1.1", optional = true}
pre-commit = {version = ">=3.7.0", optional = true}
debugpy = {version = ">=1.8.1", optional = true}
pytest = {version = ">=8.1.0", optional = true}
pytest-cov = {version = ">=5.0.0", optional = true}
datasets = "^2.19.0"
imagecodecs = { version = "^2024.1.1", optional = true }
pyav = "^12.0.5"
moviepy = "^1.0.3"
rerun-sdk = "^0.15.1"
imagecodecs = { version = ">=2024.1.1", optional = true }
pyav = ">=12.0.5"
moviepy = ">=1.0.3"
rerun-sdk = ">=0.15.1"
[tool.poetry.extras]
@ -104,5 +104,5 @@ ignore-init-module-imports = true
[build-system]
requires = ["poetry-core>=1.5.0"]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"

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3
tests/test_examples.py
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@ -3,6 +3,8 @@ import subprocess
import sys
from pathlib import Path
from tests.utils import require_package
def _find_and_replace(text: str, finds_and_replaces: list[tuple[str, str]]) -> str:
for f, r in finds_and_replaces:
@ -21,6 +23,7 @@ def test_example_1():
assert Path("outputs/examples/1_load_lerobot_dataset/episode_0.mp4").exists()
@require_package("gym_pusht")
def test_examples_3_and_2():
"""
Train a model with example 3, check the outputs.

2
tests/test_policies.py
View File

@ -236,7 +236,7 @@ def test_normalize(insert_temporal_dim):
@pytest.mark.parametrize(
"env_name, policy_name, extra_overrides",
[
# ("xarm", "tdmpc", ["policy.n_action_repeats=2"]),
("xarm", "tdmpc", []),
(
"pusht",
"diffusion",

38
tests/test_utils.py Normal file
View File

@ -0,0 +1,38 @@
import random
from typing import Callable
import numpy as np
import pytest
import torch
from lerobot.common.utils.utils import seeded_context, set_global_seed
@pytest.mark.parametrize(
"rand_fn",
[
random.random,
np.random.random,
lambda: torch.rand(1).item(),
]
+ [lambda: torch.rand(1, device="cuda")]
if torch.cuda.is_available()
else [],
)
def test_seeding(rand_fn: Callable[[], int]):
set_global_seed(0)
a = rand_fn()
with seeded_context(1337):
c = rand_fn()
b = rand_fn()
set_global_seed(0)
a_ = rand_fn()
b_ = rand_fn()
# Check that `set_global_seed` lets us reproduce a and b.
assert a_ == a
# Additionally, check that the `seeded_context` didn't interrupt the global RNG.
assert b_ == b
set_global_seed(1337)
c_ = rand_fn()
# Check that `seeded_context` and `global_seed` give the same reproducibility.
assert c_ == c

19
tests/utils.py
View File

@ -1,4 +1,5 @@
import platform
from functools import wraps
import pytest
import torch
@ -61,7 +62,6 @@ def require_env(func):
Decorator that skips the test if the required environment package is not installed.
As it need 'env_name' in args, it also checks whether it is provided as an argument.
"""
from functools import wraps
@wraps(func)
def wrapper(*args, **kwargs):
@ -82,3 +82,20 @@ def require_env(func):
return func(*args, **kwargs)
return wrapper
def require_package(package_name):
"""
Decorator that skips the test if the specified package is not installed.
"""
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
if not is_package_available(package_name):
pytest.skip(f"{package_name} not installed")
return func(*args, **kwargs)
return wrapper
return decorator