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https://github.com/huggingface/lerobot.git
synced 2026-05-30 18:31:25 +00:00
Compare commits
1 Commits
fix/re-ena
...
security-f
| Author | SHA1 | Date | |
|---|---|---|---|
|
ecf3f8a04e |
3
.github/workflows/fast_tests.yml
vendored
3
.github/workflows/fast_tests.yml
vendored
@@ -44,7 +44,7 @@ permissions:
|
||||
# Sets up the environment variables
|
||||
env:
|
||||
UV_VERSION: "0.8.0"
|
||||
PYTHON_VERSION: "3.12"
|
||||
PYTHON_VERSION: "3.10"
|
||||
|
||||
# Ensures that only the latest commit for a PR or branch is built, canceling older runs.
|
||||
concurrency:
|
||||
@@ -91,7 +91,6 @@ jobs:
|
||||
run: uv sync --extra "test"
|
||||
|
||||
- name: Login to Hugging Face
|
||||
if: env.HF_USER_TOKEN != ''
|
||||
run: |
|
||||
uv run hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
|
||||
uv run hf auth whoami
|
||||
|
||||
9
.github/workflows/full_tests.yml
vendored
9
.github/workflows/full_tests.yml
vendored
@@ -37,7 +37,7 @@ permissions:
|
||||
# Sets up the environment variables
|
||||
env:
|
||||
UV_VERSION: "0.8.0"
|
||||
PYTHON_VERSION: "3.12"
|
||||
PYTHON_VERSION: "3.10"
|
||||
DOCKER_IMAGE_NAME: huggingface/lerobot-gpu
|
||||
|
||||
# Ensures that only the latest action is built, canceling older runs.
|
||||
@@ -89,7 +89,6 @@ jobs:
|
||||
run: uv sync --extra all # TODO(Steven): Make flash-attn optional
|
||||
|
||||
- name: Login to Hugging Face
|
||||
if: env.HF_USER_TOKEN != ''
|
||||
run: |
|
||||
uv run hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
|
||||
uv run hf auth whoami
|
||||
@@ -182,12 +181,11 @@ jobs:
|
||||
working-directory: /lerobot
|
||||
steps:
|
||||
- name: Login to Hugging Face
|
||||
if: env.HF_USER_TOKEN != ''
|
||||
run: |
|
||||
hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
|
||||
hf auth whoami
|
||||
- name: Fix ptxas permissions
|
||||
run: chmod +x /lerobot/.venv/lib/python3.12/site-packages/triton/backends/nvidia/bin/ptxas
|
||||
run: chmod +x /lerobot/.venv/lib/python3.10/site-packages/triton/backends/nvidia/bin/ptxas
|
||||
- name: Run pytest on GPU
|
||||
run: pytest tests -vv --maxfail=10
|
||||
- name: Run end-to-end tests
|
||||
@@ -202,7 +200,6 @@ jobs:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Get Docker Hub Token and Delete Image
|
||||
# zizmor: ignore[template-injection]
|
||||
env:
|
||||
DOCKERHUB_LEROBOT_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
|
||||
DOCKERHUB_LEROBOT_PASSWORD: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
|
||||
@@ -234,4 +231,4 @@ jobs:
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# TODO(Steven): Check dockerimages pull in ubuntu
|
||||
# TODO(Steven): Check dockerimages pull in ubuntu
|
||||
24
.github/workflows/nightly.yml
vendored
24
.github/workflows/nightly.yml
vendored
@@ -28,7 +28,7 @@ on:
|
||||
# Sets up the environment variables
|
||||
env:
|
||||
UV_VERSION: "0.8.0"
|
||||
PYTHON_VERSION: "3.12"
|
||||
PYTHON_VERSION: "3.10"
|
||||
DOCKER_IMAGE_NAME_CPU: huggingface/lerobot-cpu:latest
|
||||
DOCKER_IMAGE_NAME_GPU: huggingface/lerobot-gpu:latest
|
||||
|
||||
@@ -119,7 +119,6 @@ jobs:
|
||||
HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot
|
||||
TORCH_HOME: /home/user_lerobot/.cache/torch
|
||||
TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
|
||||
HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
|
||||
container:
|
||||
image: ${{ needs.build-docker-cpu-nightly.outputs.image_tag }} # zizmor: ignore[unpinned-images]
|
||||
options: --shm-size "16gb"
|
||||
@@ -131,11 +130,6 @@ jobs:
|
||||
shell: bash
|
||||
working-directory: /lerobot
|
||||
steps:
|
||||
- name: Login to Hugging Face
|
||||
if: env.HF_USER_TOKEN != ''
|
||||
run: |
|
||||
hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
|
||||
hf auth whoami
|
||||
- name: Run pytest on CPU
|
||||
run: pytest tests -vv --maxfail=10
|
||||
- name: Run end-to-end tests
|
||||
@@ -152,7 +146,6 @@ jobs:
|
||||
HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot
|
||||
TORCH_HOME: /home/user_lerobot/.cache/torch
|
||||
TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
|
||||
HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
|
||||
container:
|
||||
image: ${{ needs.build-docker-gpu-nightly.outputs.image_tag }} # zizmor: ignore[unpinned-images]
|
||||
options: --gpus all --shm-size "16gb"
|
||||
@@ -164,11 +157,6 @@ jobs:
|
||||
shell: bash
|
||||
working-directory: /lerobot
|
||||
steps:
|
||||
- name: Login to Hugging Face
|
||||
if: env.HF_USER_TOKEN != ''
|
||||
run: |
|
||||
hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
|
||||
hf auth whoami
|
||||
- name: Run pytest on GPU
|
||||
run: pytest tests -vv --maxfail=10
|
||||
- name: Run end-to-end tests
|
||||
@@ -186,7 +174,6 @@ jobs:
|
||||
TORCH_HOME: /home/user_lerobot/.cache/torch
|
||||
TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
|
||||
CUDA_VISIBLE_DEVICES: "0,1,2,3"
|
||||
HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
|
||||
container:
|
||||
image: ${{ needs.build-docker-gpu-nightly.outputs.image_tag }} # zizmor: ignore[unpinned-images]
|
||||
options: --gpus all --shm-size "16gb"
|
||||
@@ -198,15 +185,12 @@ jobs:
|
||||
shell: bash
|
||||
working-directory: /lerobot
|
||||
steps:
|
||||
- name: Login to Hugging Face
|
||||
if: env.HF_USER_TOKEN != ''
|
||||
run: |
|
||||
hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
|
||||
hf auth whoami
|
||||
- name: Verify GPU availability
|
||||
run: |
|
||||
nvidia-smi
|
||||
python -c "import torch; print(f'PyTorch CUDA available: {torch.cuda.is_available()}'); print(f'Number of GPUs: {torch.cuda.device_count()}')"
|
||||
|
||||
- name: Run multi-GPU training tests
|
||||
run: pytest -vv tests/training/
|
||||
# TODO(Steven): Investigate why motors tests are failing in multi-GPU setup
|
||||
run: pytest tests -vv --maxfail=10 --ignore=tests/motors/
|
||||
timeout-minutes: 10
|
||||
|
||||
2
.github/workflows/quality.yml
vendored
2
.github/workflows/quality.yml
vendored
@@ -50,7 +50,7 @@ jobs:
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v6
|
||||
with:
|
||||
python-version: '3.12'
|
||||
python-version: '3.10'
|
||||
|
||||
- name: Run pre-commit hooks
|
||||
uses: pre-commit/action@v3.0.1 # zizmor: ignore[unpinned-uses]
|
||||
|
||||
12
.github/workflows/release.yml
vendored
12
.github/workflows/release.yml
vendored
@@ -22,7 +22,7 @@ on:
|
||||
# Sets up the environment variables
|
||||
env:
|
||||
UV_VERSION: "0.8.0"
|
||||
PYTHON_VERSION: "3.12"
|
||||
PYTHON_VERSION: "3.10"
|
||||
|
||||
jobs:
|
||||
# This job builds the Python package and publishes it to PyPI
|
||||
@@ -45,7 +45,7 @@ jobs:
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v6
|
||||
with:
|
||||
python-version: '3.12'
|
||||
python-version: '3.10'
|
||||
|
||||
- name: Extract Version
|
||||
id: extract_info
|
||||
@@ -83,6 +83,14 @@ jobs:
|
||||
exit 1
|
||||
fi
|
||||
|
||||
- name: Remove Tags with Git dependencies
|
||||
# TODO(Steven): Temporary patch to remove pi from PyPi 0.4.0 release due to its reliance on git dependencies.
|
||||
run: |
|
||||
echo "::info:: Checking for Git dependencies to remove from pyproject.toml..."
|
||||
grep -E '@ git\+https|lerobot\[pi\]' pyproject.toml | sed 's/^/::warning:: Removing line: /' || true
|
||||
sed -E -i '/@ git\+https|lerobot\[pi\]/d' pyproject.toml
|
||||
echo "::info:: Git dependencies removed. Proceeding with build."
|
||||
|
||||
- name: Install build dependencies
|
||||
run: python -m pip install build
|
||||
|
||||
|
||||
15
.github/workflows/unbound_deps_tests.yml
vendored
15
.github/workflows/unbound_deps_tests.yml
vendored
@@ -29,7 +29,7 @@ permissions:
|
||||
# Sets up the environment variables
|
||||
env:
|
||||
UV_VERSION: "0.8.0"
|
||||
PYTHON_VERSION: "3.12"
|
||||
PYTHON_VERSION: "3.10"
|
||||
DOCKER_IMAGE_NAME: huggingface/lerobot-gpu:unbound
|
||||
|
||||
# Ensures that only the latest action is built, canceling older runs.
|
||||
@@ -48,7 +48,6 @@ jobs:
|
||||
MUJOCO_GL: egl
|
||||
HF_HOME: /mnt/cache/.cache/huggingface
|
||||
HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
|
||||
HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
|
||||
steps:
|
||||
- uses: actions/checkout@v6
|
||||
with:
|
||||
@@ -80,11 +79,7 @@ jobs:
|
||||
|
||||
- name: Install lerobot with all extras
|
||||
run: uv sync --extra all # TODO(Steven): Make flash-attn optional
|
||||
- name: Login to Hugging Face
|
||||
if: env.HF_USER_TOKEN != ''
|
||||
run: |
|
||||
uv run hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
|
||||
uv run hf auth whoami
|
||||
|
||||
- name: Run pytest (all extras)
|
||||
run: uv run pytest tests -vv
|
||||
|
||||
@@ -142,7 +137,6 @@ jobs:
|
||||
HF_LEROBOT_HOME: /home/user_lerobot/.cache/huggingface/lerobot
|
||||
TORCH_HOME: /home/user_lerobot/.cache/torch
|
||||
TRITON_CACHE_DIR: /home/user_lerobot/.cache/triton
|
||||
HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
|
||||
container:
|
||||
image: ${{ needs.build-and-push-docker.outputs.image_tag }} # zizmor: ignore[unpinned-images]
|
||||
options: --gpus all --shm-size "16gb"
|
||||
@@ -154,11 +148,6 @@ jobs:
|
||||
shell: bash
|
||||
working-directory: /lerobot
|
||||
steps:
|
||||
- name: Login to Hugging Face
|
||||
if: env.HF_USER_TOKEN != ''
|
||||
run: |
|
||||
hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
|
||||
hf auth whoami
|
||||
- name: Run pytest on GPU
|
||||
run: pytest tests -vv
|
||||
- name: Run end-to-end tests
|
||||
|
||||
@@ -13,7 +13,7 @@
|
||||
# limitations under the License.
|
||||
|
||||
default_language_version:
|
||||
python: python3.12
|
||||
python: python3.10
|
||||
|
||||
exclude: "tests/artifacts/.*\\.safetensors$"
|
||||
|
||||
@@ -55,7 +55,7 @@ repos:
|
||||
rev: v3.21.0
|
||||
hooks:
|
||||
- id: pyupgrade
|
||||
args: [--py312-plus]
|
||||
args: [--py310-plus]
|
||||
|
||||
##### Markdown Quality #####
|
||||
- repo: https://github.com/rbubley/mirrors-prettier
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
|
||||
Everyone is welcome to contribute, and we value everybody's contribution. Code is not the only way to help the community. Answering questions, helping others, reaching out, and improving the documentation are immensely valuable.
|
||||
|
||||
Whichever way you choose to contribute, please be mindful to respect our [code of conduct](https://github.com/huggingface/lerobot/blob/main/CODE_OF_CONDUCT.md) and our [AI policy](https://github.com/huggingface/lerobot/blob/main/AI_POLICY.md).
|
||||
Whichever way you choose to contribute, please be mindful to respect our [code of conduct](./CODE_OF_CONDUCT.md) and our [AI policy](./AI_POLICY.md).
|
||||
|
||||
## Ways to Contribute
|
||||
|
||||
@@ -32,7 +32,7 @@ git remote add upstream https://github.com/huggingface/lerobot.git
|
||||
|
||||
### 2. Environment Installation
|
||||
|
||||
Please follow our [Installation Guide](https://huggingface.co/docs/lerobot/installation) for the environment setup & installation from source.
|
||||
Please follow our [Installation Guide](./docs/source/installation.mdx) for the environment setup & installation from source.
|
||||
|
||||
## Running Tests & Quality Checks
|
||||
|
||||
@@ -75,8 +75,8 @@ pytest -sv tests/test_specific_feature.py
|
||||
|
||||
Use the templates for required fields and examples.
|
||||
|
||||
- **Issues:** Follow the [ticket template](https://github.com/huggingface/lerobot/blob/main/.github/ISSUE_TEMPLATE/bug-report.yml).
|
||||
- **Pull requests:** Rebase on `upstream/main`, use a descriptive branch (don't work on `main`), run `pre-commit` and tests locally, and follow the [PR template](https://github.com/huggingface/lerobot/blob/main/.github/PULL_REQUEST_TEMPLATE.md).
|
||||
- **Issues:** Follow the [ticket template](./.github/ISSUE_TEMPLATE/bug-report.yml).
|
||||
- **Pull requests:** Rebase on `upstream/main`, use a descriptive branch (don't work on `main`), run `pre-commit` and tests locally, and follow the [PR template](./.github/PULL_REQUEST_TEMPLATE.md).
|
||||
|
||||
One member of the LeRobot team will then review your contribution.
|
||||
|
||||
|
||||
21
README.md
21
README.md
@@ -135,7 +135,7 @@ Learn how to implement your own simulation environment or benchmark and distribu
|
||||
|
||||
## Citation
|
||||
|
||||
If you use LeRobot in your project, please cite the GitHub repository to acknowledge the ongoing development and contributors:
|
||||
If you use LeRobot in your research, please cite:
|
||||
|
||||
```bibtex
|
||||
@misc{cadene2024lerobot,
|
||||
@@ -146,26 +146,9 @@ If you use LeRobot in your project, please cite the GitHub repository to acknowl
|
||||
}
|
||||
```
|
||||
|
||||
If you are referencing our research or the academic paper, please also cite our ICLR publication:
|
||||
|
||||
<details>
|
||||
<summary><b>ICLR 2026 Paper</b></summary>
|
||||
|
||||
```bibtex
|
||||
@inproceedings{cadenelerobot,
|
||||
title={LeRobot: An Open-Source Library for End-to-End Robot Learning},
|
||||
author={Cadene, Remi and Alibert, Simon and Capuano, Francesco and Aractingi, Michel and Zouitine, Adil and Kooijmans, Pepijn and Choghari, Jade and Russi, Martino and Pascal, Caroline and Palma, Steven and Shukor, Mustafa and Moss, Jess and Soare, Alexander and Aubakirova, Dana and Lhoest, Quentin and Gallou\'edec, Quentin and Wolf, Thomas},
|
||||
booktitle={The Fourteenth International Conference on Learning Representations},
|
||||
year={2026},
|
||||
url={https://arxiv.org/abs/2602.22818}
|
||||
}
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## Contribute
|
||||
|
||||
We welcome contributions from everyone in the community! To get started, please read our [CONTRIBUTING.md](https://github.com/huggingface/lerobot/blob/main/CONTRIBUTING.md) guide. Whether you're adding a new feature, improving documentation, or fixing a bug, your help and feedback are invaluable. We're incredibly excited about the future of open-source robotics and can't wait to work with you on what's next—thank you for your support!
|
||||
We welcome contributions from everyone in the community! To get started, please read our [CONTRIBUTING.md](./CONTRIBUTING.md) guide. Whether you're adding a new feature, improving documentation, or fixing a bug, your help and feedback are invaluable. We're incredibly excited about the future of open-source robotics and can't wait to work with you on what's next—thank you for your support!
|
||||
|
||||
<p align="center">
|
||||
<img alt="SO101 Video" src="./media/readme/so100_video.webp" width="640px">
|
||||
|
||||
@@ -24,7 +24,7 @@ ARG OS_VERSION=22.04
|
||||
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu${OS_VERSION}
|
||||
|
||||
# Define Python version argument
|
||||
ARG PYTHON_VERSION=3.12
|
||||
ARG PYTHON_VERSION=3.10
|
||||
|
||||
# Configure environment variables
|
||||
ENV DEBIAN_FRONTEND=noninteractive \
|
||||
|
||||
@@ -18,10 +18,8 @@
|
||||
# docker build -f docker/Dockerfile.user -t lerobot-user .
|
||||
# docker run -it --rm lerobot-user
|
||||
|
||||
# With USB physical access : docker run -it --device=/dev/ -v /dev/:/dev/ --rm lerobot-user
|
||||
|
||||
# Configure the base image
|
||||
ARG PYTHON_VERSION=3.12
|
||||
ARG PYTHON_VERSION=3.10
|
||||
FROM python:${PYTHON_VERSION}-slim
|
||||
|
||||
# Configure environment variables
|
||||
|
||||
@@ -19,8 +19,6 @@
|
||||
title: Multi GPU training
|
||||
- local: peft_training
|
||||
title: Training with PEFT (e.g., LoRA)
|
||||
- local: rename_map
|
||||
title: Using Rename Map and Empty Cameras
|
||||
title: "Tutorials"
|
||||
- sections:
|
||||
- local: lerobot-dataset-v3
|
||||
|
||||
@@ -310,4 +310,4 @@ Asynchronous inference represents a significant advancement in real-time robotic
|
||||
- **Universal Compatibility**: Works with all LeRobot-supported policies, from lightweight ACT models to vision-language models like SmolVLA
|
||||
|
||||
Start experimenting with the default parameters, monitor your action queue sizes, and iteratively refine your setup to achieve optimal performance for your specific use case.
|
||||
If you want to discuss this further, hop into our [Discord community](https://discord.gg/s3KuuzsPFb), or open an issue on our [GitHub repository](https://github.com/huggingface/lerobot/issues).
|
||||
If you want to discuss this further, hop into our [Discord community](https://discord.gg/s3KuuzsPFb), or open an issue on our [GitHub repository](https://github.com/lerobot/lerobot/issues).
|
||||
|
||||
@@ -32,7 +32,7 @@ version = "0.1.0"
|
||||
dependencies = [
|
||||
# your policy-specific dependencies
|
||||
]
|
||||
requires-python = ">= 3.12"
|
||||
requires-python = ">= 3.11"
|
||||
|
||||
[build-system]
|
||||
build-backend = # your-build-backend
|
||||
@@ -82,7 +82,7 @@ Create your policy implementation by inheriting from LeRobot's base `PreTrainedP
|
||||
# modeling_my_custom_policy.py
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from typing import Any
|
||||
from typing import Dict, Any
|
||||
|
||||
from lerobot.policies.pretrained import PreTrainedPolicy
|
||||
from .configuration_my_custom_policy import MyCustomPolicyConfig
|
||||
@@ -91,7 +91,7 @@ class MyCustomPolicy(PreTrainedPolicy):
|
||||
config_class = MyCustomPolicyConfig
|
||||
name = "my_custom_policy"
|
||||
|
||||
def __init__(self, config: MyCustomPolicyConfig, dataset_stats: dict[str, Any] = None):
|
||||
def __init__(self, config: MyCustomPolicyConfig, dataset_stats: Dict[str, Any] = None):
|
||||
super().__init__(config, dataset_stats)
|
||||
...
|
||||
```
|
||||
@@ -102,7 +102,7 @@ Create processor functions:
|
||||
|
||||
```python
|
||||
# processor_my_custom_policy.py
|
||||
from typing import Any
|
||||
from typing import Dict, Any
|
||||
import torch
|
||||
|
||||
|
||||
|
||||
@@ -13,7 +13,7 @@ The EarthRover Mini Plus is a fully open source mobile robot that connects throu
|
||||
### Hardware
|
||||
|
||||
- EarthRover Mini robot
|
||||
- Computer with Python 3.12 or newer
|
||||
- Computer with Python 3.10 or newer
|
||||
- Internet connection
|
||||
|
||||
### Setting Up the Frodobots SDK
|
||||
@@ -204,26 +204,22 @@ Replace `your_username/dataset_name` with your Hugging Face username and a name
|
||||
|
||||
Your dataset includes:
|
||||
|
||||
**Your Actions (2 features)**:
|
||||
**Your Actions (2 things)**:
|
||||
|
||||
- `linear_velocity`: How much you moved forward/backward
|
||||
- `angular_velocity`: How much you turned left/right
|
||||
- How much you moved forward/backward
|
||||
- How much you turned left/right
|
||||
|
||||
**Robot Observations (24 features)**:
|
||||
**Robot Observations (12 things)**:
|
||||
|
||||
- Front camera video
|
||||
- Rear camera video
|
||||
- Current speed
|
||||
- Battery level
|
||||
- Orientation
|
||||
- GPS (latitude, longitude, signal strength)
|
||||
- Which way the robot is facing
|
||||
- GPS location (latitude, longitude, signal strength)
|
||||
- Network signal strength
|
||||
- Vibration level
|
||||
- Lamp state (on/off)
|
||||
- Accelerometer (x, y, z)
|
||||
- Gyroscope (x, y, z)
|
||||
- Magnetometer (x, y, z)
|
||||
- Wheel RPMs (4 wheels)
|
||||
- Lamp status (on/off)
|
||||
|
||||
### Where Your Data Goes
|
||||
|
||||
|
||||
@@ -165,7 +165,7 @@ hf auth login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
|
||||
Then store your Hugging Face repository name in a variable:
|
||||
|
||||
```bash
|
||||
HF_USER=$(NO_COLOR=1 hf auth whoami | awk -F': *' 'NR==1 {print $2}')
|
||||
HF_USER=$(hf auth whoami | awk -F': *' 'NR==1 {print $2}')
|
||||
echo $HF_USER
|
||||
```
|
||||
|
||||
|
||||
@@ -1,8 +1,8 @@
|
||||
# Installation
|
||||
|
||||
This guide uses `conda` (via miniforge) to manage environments (recommended). If you prefer another environment manager (e.g. `uv`, `venv`), ensure you have Python >=3.12 and `ffmpeg` installed with the `libsvtav1` encoder, then skip ahead to [Environment Setup](#step-2-environment-setup).
|
||||
This guide uses conda (via miniforge) to manage environments. If you prefer another environment manager (e.g. `uv`, `venv`), ensure you have Python >=3.10 and ffmpeg installed with the `libsvtav1` encoder, then skip ahead to [Install LeRobot](#step-3-install-lerobot-).
|
||||
|
||||
## Step 1 (`conda` only): Install [`miniforge`](https://conda-forge.org/download/)
|
||||
## Step 1: Install [`miniforge`](https://conda-forge.org/download/)
|
||||
|
||||
```bash
|
||||
wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
|
||||
@@ -11,47 +11,22 @@ bash Miniforge3-$(uname)-$(uname -m).sh
|
||||
|
||||
## Step 2: Environment Setup
|
||||
|
||||
Create a virtual environment with Python 3.12:
|
||||
Create a virtual environment with Python 3.10, using conda:
|
||||
|
||||
<!-- prettier-ignore-start -->
|
||||
<hfoptions id="create_venv">
|
||||
<hfoption id="conda">
|
||||
```bash
|
||||
conda create -y -n lerobot python=3.12
|
||||
conda create -y -n lerobot python=3.10
|
||||
```
|
||||
</hfoption>
|
||||
<hfoption id="uv">
|
||||
|
||||
Then activate your conda environment, you have to do this each time you open a shell to use lerobot:
|
||||
|
||||
```bash
|
||||
uv python install 3.12
|
||||
uv venv --python 3.12
|
||||
```
|
||||
</hfoption>
|
||||
</hfoptions>
|
||||
<!-- prettier-ignore-end -->
|
||||
|
||||
Then activate your virtual environment, you have to do this each time you open a shell to use lerobot:
|
||||
|
||||
<!-- prettier-ignore-start -->
|
||||
<hfoptions id="activate_venv">
|
||||
<hfoption id="conda">```bash
|
||||
conda activate lerobot
|
||||
```</hfoption>
|
||||
<hfoption id="uv">
|
||||
```bash
|
||||
# Linux/macOSsource
|
||||
source .venv/bin/activate
|
||||
# Windows PowerShell
|
||||
source .venv\Scripts\Activate.ps1
|
||||
```
|
||||
</hfoption>
|
||||
</hfoptions>
|
||||
<!-- prettier-ignore-end -->
|
||||
|
||||
When using `conda`, install `ffmpeg` in your environment:
|
||||
|
||||
```bash
|
||||
conda install ffmpeg -c conda-forge
|
||||
ffmpeg -version # ffmpeg 8.X is not yet supported !
|
||||
```
|
||||
|
||||
> [!TIP]
|
||||
@@ -72,9 +47,6 @@ ffmpeg -version # ffmpeg 8.X is not yet supported !
|
||||
> conda install evdev -c conda-forge
|
||||
> ```
|
||||
|
||||
> [!IMPORTANT]
|
||||
> If you are using `uv` you will have to install `ffmpeg` system-wide (outside of the virtual environment). You rely on `uv` and `torchcodec` ability to dynamically link to the system `ffmpeg`.
|
||||
|
||||
## Step 3: Install LeRobot 🤗
|
||||
|
||||
### From Source
|
||||
@@ -88,45 +60,23 @@ cd lerobot
|
||||
|
||||
Then, install the library in editable mode. This is useful if you plan to contribute to the code.
|
||||
|
||||
<!-- prettier-ignore-start -->
|
||||
<hfoptions id="install_lerobot_src">
|
||||
<hfoption id="conda">
|
||||
```bash
|
||||
pip install -e .
|
||||
```
|
||||
</hfoption>
|
||||
<hfoption id="uv">
|
||||
```bash
|
||||
uv pip install -e .
|
||||
```
|
||||
</hfoption>
|
||||
</hfoptions>
|
||||
<!-- prettier-ignore-end -->
|
||||
|
||||
### Installation from PyPI
|
||||
|
||||
**Core Library:**
|
||||
Install the base package with:
|
||||
|
||||
<!-- prettier-ignore-start -->
|
||||
<hfoptions id="install_lerobot_pypi">
|
||||
<hfoption id="conda">
|
||||
```bash
|
||||
pip install lerobot
|
||||
```
|
||||
</hfoption>
|
||||
<hfoption id="uv">
|
||||
```bash
|
||||
uv pip install lerobot
|
||||
```
|
||||
</hfoption>
|
||||
</hfoptions>
|
||||
<!-- prettier-ignore-end -->
|
||||
|
||||
_This installs only the default dependencies._
|
||||
|
||||
**Extra Features:**
|
||||
To install additional functionality, use one of the following (If you are using `uv`, replace `pip install` with `uv pip install` in the commands below.):
|
||||
To install additional functionality, use one of the following:
|
||||
|
||||
```bash
|
||||
pip install 'lerobot[all]' # All available features
|
||||
@@ -140,10 +90,13 @@ _Replace `[...]` with your desired features._
|
||||
For a full list of optional dependencies, see:
|
||||
https://pypi.org/project/lerobot/
|
||||
|
||||
> [!NOTE]
|
||||
> For lerobot 0.4.0, if you want to install pi, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`
|
||||
|
||||
### Troubleshooting
|
||||
|
||||
If you encounter build errors, you may need to install additional dependencies: `cmake`, `build-essential`, and `ffmpeg libs`.
|
||||
To install these for Linux run:
|
||||
To install these for linux run:
|
||||
|
||||
```bash
|
||||
sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev
|
||||
@@ -153,7 +106,7 @@ For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/
|
||||
|
||||
## Optional dependencies
|
||||
|
||||
LeRobot provides optional extras for specific functionalities. Multiple extras can be combined (e.g., `.[aloha,feetech]`). For all available extras, refer to `pyproject.toml`. If you are using `uv`, replace `pip install` with `uv pip install` in the commands below.
|
||||
LeRobot provides optional extras for specific functionalities. Multiple extras can be combined (e.g., `.[aloha,feetech]`). For all available extras, refer to `pyproject.toml`.
|
||||
|
||||
### Simulations
|
||||
|
||||
|
||||
@@ -34,6 +34,11 @@ As described by Physical Intelligence, while AI has achieved remarkable success
|
||||
pip install -e ".[pi]"
|
||||
```
|
||||
|
||||
> [!NOTE]
|
||||
> For lerobot 0.4.0, if you want to install pi tag, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
|
||||
>
|
||||
> This will be solved in the next patch release
|
||||
|
||||
## Training Data and Capabilities
|
||||
|
||||
π₀ is trained on the largest robot interaction dataset to date, combining three key data sources:
|
||||
|
||||
@@ -36,6 +36,11 @@ This diverse training mixture creates a "curriculum" that enables generalization
|
||||
pip install -e ".[pi]"
|
||||
```
|
||||
|
||||
> [!NOTE]
|
||||
> For lerobot 0.4.0, if you want to install pi tag, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
|
||||
>
|
||||
> This will be solved in the next patch release
|
||||
|
||||
## Usage
|
||||
|
||||
To use π₀.₅ in your LeRobot configuration, specify the policy type as:
|
||||
|
||||
@@ -43,6 +43,11 @@ This approach can transform **any existing VLM** into a VLA by training it to pr
|
||||
pip install -e ".[pi]"
|
||||
```
|
||||
|
||||
> [!NOTE]
|
||||
> For lerobot 0.4.0, if you want to install the pi tag, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
|
||||
>
|
||||
> This will be solved in the next patch release
|
||||
|
||||
## Training a Custom FAST Tokenizer
|
||||
|
||||
You have two options for the FAST tokenizer:
|
||||
|
||||
@@ -1,114 +0,0 @@
|
||||
# Rename Map and Empty Cameras
|
||||
|
||||
When you train, evaluate, or record with a robot policy, your **dataset** or **environment** provides observations under one set of keys (e.g. `observation.images.front`, `observation.images.eagle`), while your **policy** expects another (e.g. `observation.images.image`, `observation.images.image2`). The **rename map** bridges that gap without changing the policy or data source.
|
||||
|
||||
> **Scope:** The rename map only renames **observation** keys (images and state). Action keys are not affected.
|
||||
|
||||
## Why observation keys don't always match
|
||||
|
||||
Policies have a fixed set of **input feature names** baked into their pretrained config. For example:
|
||||
|
||||
- [pi0fast-libero](https://huggingface.co/lerobot/pi0fast-libero) expects `observation.images.base_0_rgb` and `observation.images.left_wrist_0_rgb`.
|
||||
- [xvla-base](https://huggingface.co/lerobot/xvla-base) expects `observation.images.image`, `observation.images.image2`, and `observation.images.image3`.
|
||||
|
||||
Your dataset might use different names entirely (e.g. `observation.images.front`, `observation.images.eagle`, `observation.images.glove`), and your eval environment might use yet another set. Rather than editing the policy config or renaming columns in the dataset, you pass a **rename map**: a JSON dictionary that maps source keys to the keys the policy expects. Renaming happens inside the preprocessor pipeline, so the policy always sees its expected keys.
|
||||
|
||||
## Using the rename map
|
||||
|
||||
Pass the mapping as a JSON string on the command line. The convention is always:
|
||||
|
||||
```
|
||||
--rename_map='{"source_key": "policy_key", ...}'
|
||||
```
|
||||
|
||||
where **source_key** is what the dataset or environment provides, and **policy_key** is what the policy expects.
|
||||
|
||||
Only listed keys are renamed; everything else passes through unchanged. Order of entries doesn't matter.
|
||||
|
||||
Supported policies: **PI0**, **PI05**, **PI0Fast**, **SmolVLA**, and **XVLA**.
|
||||
|
||||
### Training
|
||||
|
||||
Suppose you fine-tune [lerobot/xvla-base](https://huggingface.co/lerobot/xvla-base) on a dataset with images under `observation.images.front`, `observation.images.eagle`, and `observation.images.glove`. XVLA expects `observation.images.image`, `observation.images.image2`, and `observation.images.image3`:
|
||||
|
||||
```bash
|
||||
lerobot-train \
|
||||
--dataset.repo_id=YOUR_DATASET \
|
||||
--output_dir=./outputs/xvla_training \
|
||||
--job_name=xvla_training \
|
||||
--policy.path="lerobot/xvla-base" \
|
||||
--policy.repo_id="HF_USER/xvla-your-robot" \
|
||||
--policy.dtype=bfloat16 \
|
||||
--policy.action_mode=auto \
|
||||
--steps=20000 \
|
||||
--policy.device=cuda \
|
||||
--policy.freeze_vision_encoder=false \
|
||||
--policy.freeze_language_encoder=false \
|
||||
--policy.train_policy_transformer=true \
|
||||
--policy.train_soft_prompts=true \
|
||||
--rename_map='{"observation.images.front": "observation.images.image", "observation.images.eagle": "observation.images.image2", "observation.images.glove": "observation.images.image3"}'
|
||||
```
|
||||
|
||||
### Evaluation
|
||||
|
||||
A policy that expects `observation.images.base_0_rgb` and `observation.images.left_wrist_0_rgb` (e.g. [pi0fast-libero](https://huggingface.co/lerobot/pi0fast-libero)), but the LIBERO environment returns `observation.images.image` and `observation.images.image2`:
|
||||
|
||||
```bash
|
||||
lerobot-eval \
|
||||
--policy.path=lerobot/pi0fast-libero \
|
||||
--env.type=libero \
|
||||
... \
|
||||
--rename_map='{"observation.images.image": "observation.images.base_0_rgb", "observation.images.image2": "observation.images.left_wrist_0_rgb"}'
|
||||
```
|
||||
|
||||
### Recording
|
||||
|
||||
`lerobot-record` also supports rename maps, nested under the dataset config:
|
||||
|
||||
```bash
|
||||
lerobot-record \ # When running inference
|
||||
--policy.path="<user>/smolVLA_finetuned" \
|
||||
... \
|
||||
--dataset.rename_map='{"observation.images.glove2": "observation.images.image"}'
|
||||
```
|
||||
|
||||
## Alternative: edit the policy config directly
|
||||
|
||||
If you always use the same dataset or environment, you can **edit the policy's `config.json`** so its observation keys match your data source. Then no rename map is needed.
|
||||
|
||||
The tradeoff: modifying the policy config ties it to one data source. A rename map keeps one policy usable across many datasets and environments.
|
||||
|
||||
## Empty cameras: fewer views than the policy expects
|
||||
|
||||
Some policies are built for a fixed number of image inputs. If your dataset has fewer cameras, you can set **`empty_cameras`** in the policy config instead of modifying the model architecture.
|
||||
|
||||
### How it works
|
||||
|
||||
Setting `empty_cameras=N` adds N placeholder image features to the policy config, named:
|
||||
|
||||
```
|
||||
observation.images.empty_camera_0
|
||||
observation.images.empty_camera_1
|
||||
...
|
||||
```
|
||||
|
||||
At runtime, these keys have no corresponding data in the batch. The policy fills them with masked dummy tensors (padded with `-1` for SigLIP-based vision encoders, with a zero attention mask), so the extra image slots are effectively ignored during training and inference.
|
||||
|
||||
### Example
|
||||
|
||||
XVLA-base has three visual inputs and `empty_cameras=0` by default. Your dataset only has two cameras:
|
||||
|
||||
1. Set `--policy.empty_cameras=1`.
|
||||
2. The config adds a third key: `observation.images.empty_camera_0`.
|
||||
3. Use the rename map for your two real cameras as usual.
|
||||
4. The third slot is masked out — no fake images needed in your dataset.
|
||||
|
||||
## Quick reference
|
||||
|
||||
| Goal | What to do |
|
||||
| ----------------------------------------- | --------------------------------------------------------------------------- |
|
||||
| Dataset keys ≠ policy keys | `--rename_map='{"dataset_key": "policy_key", ...}'` |
|
||||
| Env keys ≠ policy keys (eval) | `--rename_map='{"env_key": "policy_key", ...}'` |
|
||||
| Recording with different keys (inference) | `--dataset.rename_map='{"source_key": "policy_key", ...}'`. |
|
||||
| Fewer cameras than policy expects | `--policy.empty_cameras=N` (supported by PI0, PI05, PI0Fast, SmolVLA, XVLA) |
|
||||
| Avoid passing a rename map | Edit the policy's `config.json` so its keys match your data source |
|
||||
@@ -1,72 +1,23 @@
|
||||
# Unitree G1
|
||||
|
||||
<img
|
||||
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/unitree_thumbnail.jpg"
|
||||
alt="Unitree G1 locomanipulation demo"
|
||||
style={{ width: "100%" }}
|
||||
/>
|
||||
This guide covers the complete setup process for the Unitree G1 humanoid, from initial connection to running gr00t_wbc locomotion.
|
||||
|
||||
The Unitree G1 humanoid is now supported in LeRobot! You can teleoperate, train locomanipulation policies, test in sim, and more. Both 29 and 23 DoF variants are supported.
|
||||
## About
|
||||
|
||||
We support both 29 and 23 DOF G1 EDU version. We introduce:
|
||||
|
||||
- **`unitree g1` robot class, handling low level read/write from/to the humanoid**
|
||||
- **ZMQ socket bridge** for remote communication and camera streaming, allowing for remote policy deployment over wlan, eth or directly on the robot
|
||||
- **Locomotion policies** from NVIDIA gr00t and Amazon FAR Holosoma
|
||||
- **Simulation mode** for testing policies without the physical robot in mujoco
|
||||
|
||||
---
|
||||
|
||||
## Part 1: Getting Started
|
||||
## Connection guide
|
||||
|
||||
### Install the Unitree SDK
|
||||
### Step 1: Configure Ethernet Interface
|
||||
|
||||
Follow the [unitree_sdk2_python installation guide](https://github.com/unitreerobotics/unitree_sdk2_python#installation). Tested with `unitree_sdk2py==1.0.1` and `cyclonedds==0.10.2`:
|
||||
|
||||
```bash
|
||||
conda create -y -n lerobot python=3.12
|
||||
conda activate lerobot
|
||||
git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
|
||||
cd unitree_sdk2_python
|
||||
pip install -e .
|
||||
cd ..
|
||||
```
|
||||
|
||||
### Install LeRobot
|
||||
|
||||
```bash
|
||||
conda install ffmpeg -c conda-forge
|
||||
conda install -c conda-forge "pinocchio>=3.0.0,<4.0.0"
|
||||
git clone https://github.com/huggingface/lerobot.git
|
||||
cd lerobot
|
||||
pip install -e '.[unitree_g1]'
|
||||
```
|
||||
|
||||
<Tip>
|
||||
For now, pinocchio must be installed from conda-forge (not pip) to include the
|
||||
CasADi bindings needed for arm IK.
|
||||
</Tip>
|
||||
|
||||
### Test the Installation (Simulation)
|
||||
|
||||
The simulation environment has its own dependencies. Check the Simulation environment dependencies: [Unitree G1 Mujoco EnvHub](https://huggingface.co/lerobot/unitree-g1-mujoco/tree/main).
|
||||
|
||||
```bash
|
||||
pip install mujoco loguru msgpack msgpack-numpy
|
||||
```
|
||||
|
||||
```bash
|
||||
lerobot-teleoperate \
|
||||
--robot.type=unitree_g1 \
|
||||
--robot.is_simulation=true \
|
||||
--teleop.type=unitree_g1 \
|
||||
--teleop.id=wbc_unitree \
|
||||
--robot.cameras='{"global_view": {"type": "zmq", "server_address": "localhost", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30, "warmup_s": 5}}' \
|
||||
--display_data=true \
|
||||
--robot.controller=GrootLocomotionController
|
||||
```
|
||||
|
||||
This will launch a [MuJoCo sim instance](https://huggingface.co/lerobot/unitree-g1-mujoco/tree/main) for the G1. You can connect a gamepad to your machine before launching in order to control the robot's locomotion in sim. We support both [HolosomaLocomotionController](https://github.com/amazon-far/holosoma) and [GrootLocomotionController](https://github.com/NVlabs/GR00T-WholeBodyControl) via `--robot.controller`.
|
||||
|
||||
- Press `9` to release the robot
|
||||
- Press `7` / `8` to increase / decrease waist height
|
||||
|
||||
### Connect to the Physical Robot
|
||||
|
||||
The G1's Ethernet IP is fixed at `192.168.123.164`. Your machine must have a static IP on the same subnet: `192.168.123.x` where `x ≠ 164`.
|
||||
Set a static IP on the same subnet as the robot:
|
||||
|
||||
```bash
|
||||
# Replace 'enp131s0' with your ethernet interface name (check with `ip a`)
|
||||
@@ -75,23 +26,47 @@ sudo ip addr add 192.168.123.200/24 dev enp131s0
|
||||
sudo ip link set enp131s0 up
|
||||
```
|
||||
|
||||
### SSH into the Robot
|
||||
**Note**: The G1's Ethernet IP is fixed at `192.168.123.164`. Your computer must use `192.168.123.x` with x ≠ 164.
|
||||
|
||||
### Step 2: SSH into the Robot
|
||||
|
||||
```bash
|
||||
ssh unitree@192.168.123.164
|
||||
# Password: 123
|
||||
```
|
||||
|
||||
### Share Internet via Ethernet
|
||||
You should now be connected to the G1's Orin.
|
||||
|
||||
The G1 needs internet access to clone repos and install packages. Share your laptop's connection over Ethernet:
|
||||
---
|
||||
|
||||
## Part 2: Enable WiFi on the Robot
|
||||
|
||||
Wlan0 is disabled by default on the G1. To enable it:
|
||||
|
||||
### Step 1: Enable WiFi Hardware
|
||||
|
||||
```bash
|
||||
sudo rfkill unblock wifi
|
||||
sudo rfkill unblock all
|
||||
|
||||
# Bring up wlan0
|
||||
sudo ip link set wlan0 up
|
||||
|
||||
# Enable NetworkManager control of wlan0
|
||||
sudo nmcli radio wifi on
|
||||
sudo nmcli device set wlan0 managed yes
|
||||
sudo systemctl restart NetworkManager
|
||||
```
|
||||
|
||||
### Step 2: Enable Internet Forwarding
|
||||
|
||||
**On your laptop:**
|
||||
|
||||
```bash
|
||||
# Enable IP forwarding
|
||||
sudo sysctl -w net.ipv4.ip_forward=1
|
||||
|
||||
# Replace wlp132s0f0 with your WiFi interface name
|
||||
# Set up NAT (replace wlp132s0f0 with your WiFi interface)
|
||||
sudo iptables -t nat -A POSTROUTING -o wlp132s0f0 -s 192.168.123.0/24 -j MASQUERADE
|
||||
sudo iptables -A FORWARD -i wlp132s0f0 -o enp131s0 -m state --state RELATED,ESTABLISHED -j ACCEPT
|
||||
sudo iptables -A FORWARD -i enp131s0 -o wlp132s0f0 -j ACCEPT
|
||||
@@ -100,193 +75,223 @@ sudo iptables -A FORWARD -i enp131s0 -o wlp132s0f0 -j ACCEPT
|
||||
**On the G1:**
|
||||
|
||||
```bash
|
||||
# Add laptop as default gateway
|
||||
sudo ip route del default 2>/dev/null || true
|
||||
sudo ip route add default via 192.168.123.200 dev eth0
|
||||
echo "nameserver 8.8.8.8" | sudo tee /etc/resolv.conf
|
||||
|
||||
# Verify
|
||||
# Test connection
|
||||
ping -c 3 8.8.8.8
|
||||
```
|
||||
|
||||
### Install the Unitree SDK on the G1
|
||||
|
||||
Follow the [unitree_sdk2_python installation guide](https://github.com/unitreerobotics/unitree_sdk2_python#installation):
|
||||
|
||||
```bash
|
||||
conda create -y -n lerobot python=3.12
|
||||
conda activate lerobot
|
||||
git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
|
||||
cd unitree_sdk2_python
|
||||
python -m pip install -e .
|
||||
cd ..
|
||||
```
|
||||
|
||||
### Install LeRobot on the G1
|
||||
|
||||
```bash
|
||||
git clone https://github.com/huggingface/lerobot.git
|
||||
cd lerobot
|
||||
conda install -c conda-forge "pinocchio>=3.0.0,<4.0.0"
|
||||
python -m pip install -e '.[unitree_g1]'
|
||||
```
|
||||
|
||||
<Tip>
|
||||
For now, pinocchio must be installed from conda-forge (not pip) to include the
|
||||
CasADi bindings needed for arm IK.
|
||||
</Tip>
|
||||
|
||||
### (Optional) Enable WiFi on the Robot
|
||||
|
||||
For wireless SSH access, you can enable WiFi on the G1 (it's blocked by default):
|
||||
|
||||
```bash
|
||||
sudo rfkill unblock all
|
||||
sudo ip link set wlan0 up
|
||||
sudo nmcli radio wifi on
|
||||
sudo nmcli device set wlan0 managed yes
|
||||
sudo systemctl restart NetworkManager
|
||||
```
|
||||
|
||||
**Connect to a WiFi network:**
|
||||
### Step 3: Connect to WiFi Network
|
||||
|
||||
```bash
|
||||
# List available networks
|
||||
nmcli device wifi list
|
||||
|
||||
# Connect to your WiFi (example)
|
||||
sudo nmcli connection add type wifi ifname wlan0 con-name "YourNetwork" ssid "YourNetwork"
|
||||
sudo nmcli connection modify "YourNetwork" wifi-sec.key-mgmt wpa-psk
|
||||
sudo nmcli connection modify "YourNetwork" wifi-sec.psk "YourPassword"
|
||||
sudo nmcli connection modify "YourNetwork" connection.autoconnect yes
|
||||
sudo nmcli connection up "YourNetwork"
|
||||
|
||||
# Check WiFi IP address
|
||||
ip a show wlan0
|
||||
```
|
||||
|
||||
You can then SSH over WiFi instead of Ethernet:
|
||||
### Step 4: SSH Over WiFi
|
||||
|
||||
Once connected to WiFi, note the robot's IP address and disconnect the Ethernet cable. You can now SSH over WiFi:
|
||||
|
||||
```bash
|
||||
ssh unitree@<ROBOT_WIFI_IP>
|
||||
ssh unitree@<YOUR_ROBOT_IP>
|
||||
# Password: 123
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Part 2: Teleoperation & Locomotion
|
||||
|
||||
### Run the Robot Server
|
||||
|
||||
On the robot (from `~/lerobot`):
|
||||
|
||||
```bash
|
||||
cd ~/lerobot
|
||||
python src/lerobot/robots/unitree_g1/run_g1_server.py --camera
|
||||
```
|
||||
|
||||
### Run the Locomotion Policy
|
||||
|
||||
You can run the teleoperation client from your laptop over Ethernet, over WiFi (experimental), or directly on the robot itself. Mind potential latency introduced by your network.
|
||||
|
||||
**From your laptop:**
|
||||
|
||||
```bash
|
||||
lerobot-teleoperate \
|
||||
--robot.type=unitree_g1 \
|
||||
--robot.is_simulation=false \
|
||||
--robot.robot_ip=<ROBOT_IP> \
|
||||
--teleop.type=unitree_g1 \
|
||||
--teleop.id=wbc_unitree \
|
||||
--robot.cameras='{"global_view": {"type": "zmq", "server_address": "<ROBOT_IP>", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
|
||||
--display_data=true \
|
||||
--robot.controller=HolosomaLocomotionController
|
||||
```
|
||||
|
||||
We support both [GrootLocomotionController](https://github.com/NVlabs/GR00T-WholeBodyControl) and [HolosomaLocomotionController](https://github.com/amazon-far/holosoma) via `--robot.controller`.
|
||||
Replace `<YOUR_ROBOT_IP>` with your robot's actual WiFi IP address.
|
||||
|
||||
---
|
||||
|
||||
## Part 3: Loco-Manipulation with the Homunculus Exoskeleton
|
||||
## Part 3: Robot Server Setup
|
||||
|
||||
We provide a loco-manipulation solution via the Homunculus Exoskeleton — an open-source 7 DoF exoskeleton for whole-body control. Check it out [here](https://github.com/nepyope/hmc_exo).
|
||||
### Step 1: Install LeRobot on the Orin
|
||||
|
||||
### Calibrate
|
||||
SSH into the robot and install LeRobot:
|
||||
|
||||
```bash
|
||||
ssh unitree@<YOUR_ROBOT_IP>
|
||||
|
||||
conda create -y -n lerobot python=3.10
|
||||
conda activate lerobot
|
||||
git clone https://github.com/huggingface/lerobot.git
|
||||
cd lerobot
|
||||
pip install -e '.[unitree_g1]'
|
||||
git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
|
||||
cd unitree_sdk2_python && pip install -e .
|
||||
```
|
||||
|
||||
**Note**: The Unitree SDK requires CycloneDDS v0.10.2 to be installed. See the [Unitree SDK documentation](https://github.com/unitreerobotics/unitree_sdk2_python) for details.
|
||||
|
||||
### Step 2: Run the Robot Server
|
||||
|
||||
On the robot:
|
||||
|
||||
```bash
|
||||
python src/lerobot/robots/unitree_g1/run_g1_server.py
|
||||
```
|
||||
|
||||
**Important**: Keep this terminal running. The server must be active for remote control.
|
||||
|
||||
---
|
||||
|
||||
## Part 4: Controlling the robot
|
||||
|
||||
With the robot server running, you can now control the robot remotely. Let's launch a locomotion policy
|
||||
|
||||
### Step 1: Install LeRobot on your machine
|
||||
|
||||
```bash
|
||||
conda create -y -n lerobot python=3.10
|
||||
conda activate lerobot
|
||||
git clone https://github.com/huggingface/lerobot.git
|
||||
cd lerobot
|
||||
pip install -e '.[unitree_g1]'
|
||||
git clone https://github.com/unitreerobotics/unitree_sdk2_python.git
|
||||
cd unitree_sdk2_python && pip install -e .
|
||||
```
|
||||
|
||||
### Step 2: Update Robot IP in Config
|
||||
|
||||
Edit the config file to match your robot's WiFi IP:
|
||||
|
||||
```python
|
||||
# In src/lerobot/robots/unitree_g1/config_unitree_g1.py
|
||||
robot_ip: str = "<YOUR_ROBOT_IP>" # Replace with your robot's WiFi IP.
|
||||
```
|
||||
|
||||
### Step 3: Run the Locomotion Policy
|
||||
|
||||
```bash
|
||||
# Run GR00T locomotion controller
|
||||
python examples/unitree_g1/gr00t_locomotion.py --repo-id "nepyope/GR00T-WholeBodyControl_g1"
|
||||
|
||||
# Run Holosoma locomotion controller
|
||||
python examples/unitree_g1/holosoma_locomotion.py
|
||||
|
||||
```
|
||||
|
||||
Press `Ctrl+C` to stop the policy.
|
||||
|
||||
---
|
||||
|
||||
## Running in Simulation Mode (MuJoCo)
|
||||
|
||||
You can test policies before deploying on the physical robot using MuJoCo simulation. Set `is_simulation=True` in config or pass `--robot.is_simulation=true` via CLI.
|
||||
|
||||
### Calibrate Exoskeleton Teleoperator
|
||||
|
||||
```bash
|
||||
lerobot-calibrate \
|
||||
--teleop.type=unitree_g1 \
|
||||
--teleop.left_arm_config.port=/dev/ttyACM1 \
|
||||
--teleop.right_arm_config.port=/dev/ttyACM0 \
|
||||
--teleop.id=exo
|
||||
--teleop.type=unitree_g1 \
|
||||
--teleop.left_arm_config.port=/dev/ttyACM1 \
|
||||
--teleop.right_arm_config.port=/dev/ttyACM0 \
|
||||
--teleop.id=exo
|
||||
```
|
||||
|
||||
During calibration move each joint through its entire range. After fitting, move the joint in a neutral position and press `n` to advance.
|
||||
### Teleoperate in Simulation
|
||||
|
||||
### Record a Dataset
|
||||
```bash
|
||||
lerobot-teleoperate \
|
||||
--robot.type=unitree_g1 \
|
||||
--robot.is_simulation=true \
|
||||
--teleop.type=unitree_g1 \
|
||||
--teleop.left_arm_config.port=/dev/ttyACM1 \
|
||||
--teleop.right_arm_config.port=/dev/ttyACM0 \
|
||||
--teleop.id=exo \
|
||||
--fps=100
|
||||
```
|
||||
|
||||
### Record Dataset in Simulation
|
||||
|
||||
```bash
|
||||
lerobot-record \
|
||||
--robot.type=unitree_g1 \
|
||||
--robot.is_simulation=true \
|
||||
--robot.cameras='{"global_view": {"type": "zmq", "server_address": "localhost", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
|
||||
--teleop.type=unitree_g1 \
|
||||
--teleop.left_arm_config.port=/dev/ttyACM1 \
|
||||
--teleop.right_arm_config.port=/dev/ttyACM0 \
|
||||
--teleop.id=exo \
|
||||
--dataset.repo_id=your-username/dataset-name \
|
||||
--dataset.single_task="Test" \
|
||||
--dataset.num_episodes=2 \
|
||||
--dataset.episode_time_s=5 \
|
||||
--dataset.reset_time_s=5 \
|
||||
--dataset.push_to_hub=true \
|
||||
--dataset.streaming_encoding=true \
|
||||
--dataset.encoder_threads=2
|
||||
--robot.type=unitree_g1 \
|
||||
--robot.is_simulation=true \
|
||||
--robot.cameras='{"global_view": {"type": "zmq", "server_address": "localhost", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
|
||||
--teleop.type=unitree_g1 \
|
||||
--teleop.left_arm_config.port=/dev/ttyACM1 \
|
||||
--teleop.right_arm_config.port=/dev/ttyACM0 \
|
||||
--teleop.id=exo \
|
||||
--dataset.repo_id=your-username/dataset-name \
|
||||
--dataset.single_task="Test" \
|
||||
--dataset.num_episodes=2 \
|
||||
--dataset.episode_time_s=5 \
|
||||
--dataset.reset_time_s=5 \
|
||||
--dataset.push_to_hub=true \
|
||||
--dataset.streaming_encoding=true \
|
||||
# --dataset.vcodec=auto \
|
||||
--dataset.encoder_threads=2
|
||||
```
|
||||
|
||||
> **Note:** Omit `--teleop.left_arm_config.port` and `--teleop.right_arm_config.port` if you're only using the joystick.
|
||||
|
||||
Example dataset: [nepyope/unitree_box_move_blue_full](https://huggingface.co/datasets/nepyope/unitree_box_move_blue_full)
|
||||
Example simulation dataset: [nepyope/teleop_test_sim](https://huggingface.co/datasets/nepyope/teleop_test_sim)
|
||||
|
||||
---
|
||||
|
||||
## Part 4: Training & Inference
|
||||
## Running on Real Robot
|
||||
|
||||
### Train
|
||||
Once the robot server is running on the G1 (see Part 3), you can teleoperate and record on the real robot.
|
||||
|
||||
### Start the Camera Server
|
||||
|
||||
On the robot, start the ZMQ image server:
|
||||
|
||||
```bash
|
||||
python src/lerobot/scripts/lerobot_train.py \
|
||||
--dataset.repo_id=your-username/dataset-name \
|
||||
--policy.type=pi05 \
|
||||
--output_dir=./outputs/pi05_training \
|
||||
--job_name=pi05_training \
|
||||
--policy.repo_id=your-username/your-repo-id \
|
||||
--policy.pretrained_path=lerobot/pi05_base \
|
||||
--policy.compile_model=true \
|
||||
--policy.gradient_checkpointing=true \
|
||||
--wandb.enable=true \
|
||||
--policy.dtype=bfloat16 \
|
||||
--policy.freeze_vision_encoder=false \
|
||||
--policy.train_expert_only=false \
|
||||
--steps=3000 \
|
||||
--policy.device=cuda \
|
||||
--batch_size=32
|
||||
python src/lerobot/cameras/zmq/image_server.py
|
||||
```
|
||||
|
||||
### Inference with RTC
|
||||
Keep this running in a separate terminal for camera streaming during recording.
|
||||
|
||||
Once trained, we recommend deploying policies using inference-time RTC:
|
||||
### Teleoperate Real Robot
|
||||
|
||||
```bash
|
||||
python examples/rtc/eval_with_real_robot.py \
|
||||
--policy.path=your-username/your-repo-id \
|
||||
--policy.device=cuda \
|
||||
--robot.type=unitree_g1 \
|
||||
--robot.is_simulation=false \
|
||||
--robot.controller=HolosomaLocomotionController \
|
||||
--robot.cameras='{"global_view": {"type": "zmq", "server_address": "<ROBOT_IP>", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
|
||||
--task="task_description" \
|
||||
--duration=1000 \
|
||||
--fps=30 \
|
||||
--rtc.enabled=true
|
||||
lerobot-teleoperate \
|
||||
--robot.type=unitree_g1 \
|
||||
--robot.is_simulation=false \
|
||||
--teleop.type=unitree_g1 \
|
||||
--teleop.left_arm_config.port=/dev/ttyACM1 \
|
||||
--teleop.right_arm_config.port=/dev/ttyACM0 \
|
||||
--teleop.id=exo \
|
||||
--fps=100
|
||||
```
|
||||
|
||||
### Record Dataset on Real Robot
|
||||
|
||||
```bash
|
||||
lerobot-record \
|
||||
--robot.type=unitree_g1 \
|
||||
--robot.is_simulation=false \
|
||||
--robot.cameras='{"global_view": {"type": "zmq", "server_address": "172.18.129.215", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
|
||||
--teleop.type=unitree_g1 \
|
||||
--teleop.left_arm_config.port=/dev/ttyACM1 \
|
||||
--teleop.right_arm_config.port=/dev/ttyACM0 \
|
||||
--teleop.id=exo \
|
||||
--dataset.repo_id=your-username/dataset-name \
|
||||
--dataset.single_task="Test" \
|
||||
--dataset.num_episodes=2 \
|
||||
--dataset.episode_time_s=5 \
|
||||
--dataset.reset_time_s=5 \
|
||||
--dataset.push_to_hub=true \
|
||||
--dataset.streaming_encoding=true \
|
||||
# --dataset.vcodec=auto \
|
||||
--dataset.encoder_threads=2
|
||||
```
|
||||
|
||||
**Note**: Update `server_address` to match your robot's camera server IP.
|
||||
|
||||
Example real robot dataset: [nepyope/teleop_test_real](https://huggingface.co/datasets/nepyope/teleop_test_real)
|
||||
|
||||
---
|
||||
|
||||
## Additional Resources
|
||||
@@ -295,8 +300,8 @@ python examples/rtc/eval_with_real_robot.py \
|
||||
- [GR00T-WholeBodyControl](https://github.com/NVlabs/GR00T-WholeBodyControl)
|
||||
- [Holosoma](https://github.com/amazon-far/holosoma)
|
||||
- [LeRobot Documentation](https://github.com/huggingface/lerobot)
|
||||
- [Unitree IL LeRobot](https://github.com/unitreerobotics/unitree_IL_lerobot)
|
||||
- [Unitree_IL_Lerobot](https://github.com/unitreerobotics/unitree_IL_lerobot)
|
||||
|
||||
---
|
||||
|
||||
_Last updated: March 2026_
|
||||
_Last updated: December 2025_
|
||||
|
||||
@@ -32,8 +32,7 @@ import torch
|
||||
from huggingface_hub import HfApi
|
||||
|
||||
import lerobot
|
||||
from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
|
||||
|
||||
|
||||
def main():
|
||||
|
||||
@@ -1,490 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
"""
|
||||
SLURM-distributed SARM RA-BC annotation pipeline.
|
||||
|
||||
Computes SARM progress values for all frames in a dataset, distributed across
|
||||
SLURM workers, then merges the shards into a single sarm_progress.parquet.
|
||||
|
||||
Two subcommands, each a separate SLURM submission:
|
||||
|
||||
compute – N workers, each computes progress for a subset of episodes
|
||||
aggregate – 1 worker, merges N shards into sarm_progress.parquet, pushes to hub
|
||||
|
||||
Usage:
|
||||
python slurm_compute_rabc.py compute \\
|
||||
--repo-id user/dataset --reward-model-path user/sarm_model \\
|
||||
--stride 10 --device cpu --workers 50 --partition cpu
|
||||
|
||||
python slurm_compute_rabc.py aggregate \\
|
||||
--repo-id user/dataset --reward-model-path user/sarm_model \\
|
||||
--partition cpu --push-to-hub
|
||||
"""
|
||||
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
|
||||
from datatrove.executor import LocalPipelineExecutor
|
||||
from datatrove.executor.slurm import SlurmPipelineExecutor
|
||||
from datatrove.pipeline.base import PipelineStep
|
||||
|
||||
|
||||
class ComputeProgressShards(PipelineStep):
|
||||
"""Each worker computes SARM progress for its assigned episodes."""
|
||||
|
||||
def __init__(
|
||||
self, repo_id, reward_model_path, stride=1, head_mode="sparse", device="cpu", shard_dir="rabc_shards"
|
||||
):
|
||||
super().__init__()
|
||||
if stride < 1:
|
||||
raise ValueError(f"stride must be >= 1, got {stride}")
|
||||
self.repo_id = repo_id
|
||||
self.reward_model_path = reward_model_path
|
||||
self.stride = stride
|
||||
self.head_mode = head_mode
|
||||
self.device = device
|
||||
self.shard_dir = shard_dir
|
||||
|
||||
def run(self, data=None, rank: int = 0, world_size: int = 1):
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pyarrow as pa
|
||||
import pyarrow.parquet as pq
|
||||
import torch
|
||||
from tqdm import tqdm
|
||||
|
||||
from lerobot.policies.sarm.compute_rabc_weights import (
|
||||
generate_all_frame_indices,
|
||||
interpolate_progress,
|
||||
load_sarm_resources,
|
||||
)
|
||||
from lerobot.utils.utils import init_logging
|
||||
|
||||
init_logging()
|
||||
|
||||
dataset, reward_model, preprocess = load_sarm_resources(
|
||||
self.repo_id,
|
||||
self.reward_model_path,
|
||||
self.device,
|
||||
)
|
||||
|
||||
if hasattr(preprocess, "eval"):
|
||||
preprocess.eval()
|
||||
for step in preprocess.steps:
|
||||
if hasattr(step, "eval"):
|
||||
step.eval()
|
||||
|
||||
image_key = reward_model.config.image_key
|
||||
state_key = reward_model.config.state_key
|
||||
frame_gap = reward_model.config.frame_gap
|
||||
center_idx = reward_model.config.n_obs_steps // 2
|
||||
|
||||
dual_mode = reward_model.config.uses_dual_heads
|
||||
compute_sparse = self.head_mode in ("sparse", "both") or not dual_mode
|
||||
compute_dense = self.head_mode in ("dense", "both") and dual_mode
|
||||
|
||||
my_episodes = list(range(dataset.num_episodes))[rank::world_size]
|
||||
if not my_episodes:
|
||||
logging.info(f"Rank {rank}: no episodes assigned")
|
||||
return
|
||||
logging.info(f"Rank {rank}: {len(my_episodes)} / {dataset.num_episodes} episodes")
|
||||
|
||||
all_rows = []
|
||||
|
||||
for ep_idx in tqdm(my_episodes, desc=f"Rank {rank}"):
|
||||
ep = dataset.meta.episodes[ep_idx]
|
||||
ep_start, ep_end = ep["dataset_from_index"], ep["dataset_to_index"]
|
||||
task = dataset[ep_start].get("task", "perform the task")
|
||||
|
||||
all_ep_indices = generate_all_frame_indices(ep_start, ep_end, frame_gap)
|
||||
if self.stride > 1:
|
||||
compute_indices = [i for i in all_ep_indices if (i - ep_start) % self.stride == 0]
|
||||
if (ep_end - 1) not in compute_indices:
|
||||
compute_indices.append(ep_end - 1)
|
||||
compute_indices = sorted(set(compute_indices))
|
||||
else:
|
||||
compute_indices = all_ep_indices
|
||||
|
||||
frame_results = {}
|
||||
for qi in tqdm(compute_indices, desc=f" Ep {ep_idx}", leave=False):
|
||||
try:
|
||||
sample = dataset[qi]
|
||||
batch = {
|
||||
image_key: sample[image_key],
|
||||
"task": task,
|
||||
"index": qi,
|
||||
"episode_index": ep_idx,
|
||||
}
|
||||
if state_key in sample:
|
||||
batch[state_key] = sample[state_key]
|
||||
|
||||
with torch.no_grad():
|
||||
processed = preprocess(batch)
|
||||
vf = processed["video_features"].to(self.device)
|
||||
tf = processed["text_features"].to(self.device)
|
||||
sf = processed.get("state_features")
|
||||
if sf is not None:
|
||||
sf = sf.to(self.device)
|
||||
lengths = processed.get("lengths")
|
||||
|
||||
sparse_val = dense_val = np.nan
|
||||
if compute_sparse:
|
||||
r = reward_model.calculate_rewards(
|
||||
text_embeddings=tf,
|
||||
video_embeddings=vf,
|
||||
state_features=sf,
|
||||
lengths=lengths,
|
||||
return_all_frames=True,
|
||||
head_mode="sparse",
|
||||
)
|
||||
sparse_val = float(r[0, center_idx] if r.ndim == 2 else r[center_idx])
|
||||
if compute_dense:
|
||||
r = reward_model.calculate_rewards(
|
||||
text_embeddings=tf,
|
||||
video_embeddings=vf,
|
||||
state_features=sf,
|
||||
lengths=lengths,
|
||||
return_all_frames=True,
|
||||
head_mode="dense",
|
||||
)
|
||||
dense_val = float(r[0, center_idx] if r.ndim == 2 else r[center_idx])
|
||||
|
||||
frame_results[qi] = (sparse_val, dense_val)
|
||||
except Exception as e:
|
||||
logging.warning(f"Failed frame {qi}: {e}")
|
||||
|
||||
if not frame_results:
|
||||
logging.warning(f"Episode {ep_idx}: all frames failed, skipping")
|
||||
continue
|
||||
|
||||
# Interpolate to all frames in this episode
|
||||
computed_idx = np.array(sorted(frame_results.keys()))
|
||||
all_frame_arr = np.arange(ep_start, ep_end)
|
||||
|
||||
sparse_vals = np.array([frame_results[i][0] for i in computed_idx]) if compute_sparse else None
|
||||
dense_vals = np.array([frame_results[i][1] for i in computed_idx]) if compute_dense else None
|
||||
|
||||
if self.stride > 1 and len(computed_idx) > 1:
|
||||
if compute_sparse:
|
||||
sparse_vals = interpolate_progress(computed_idx, sparse_vals, all_frame_arr)
|
||||
if compute_dense:
|
||||
dense_vals = interpolate_progress(computed_idx, dense_vals, all_frame_arr)
|
||||
output_frames = all_frame_arr
|
||||
else:
|
||||
# Use only successfully computed frames to avoid indexing mismatch on failures
|
||||
output_frames = computed_idx
|
||||
|
||||
for i, fi in enumerate(output_frames):
|
||||
row = {"index": int(fi), "episode_index": ep_idx, "frame_index": int(fi - ep_start)}
|
||||
if compute_sparse:
|
||||
row["progress_sparse"] = float(sparse_vals[i])
|
||||
if compute_dense:
|
||||
row["progress_dense"] = float(dense_vals[i])
|
||||
all_rows.append(row)
|
||||
|
||||
if all_rows:
|
||||
import pandas as pd
|
||||
|
||||
df = pd.DataFrame(all_rows).sort_values("index").reset_index(drop=True)
|
||||
table = pa.Table.from_pandas(df, preserve_index=False)
|
||||
table = table.replace_schema_metadata({b"reward_model_path": self.reward_model_path.encode()})
|
||||
shard_dir = Path(self.shard_dir)
|
||||
shard_dir.mkdir(parents=True, exist_ok=True)
|
||||
out = shard_dir / f"shard_{rank:05d}.parquet"
|
||||
pq.write_table(table, out)
|
||||
logging.info(f"Rank {rank}: saved {len(df)} rows to {out}")
|
||||
|
||||
|
||||
class AggregateProgress(PipelineStep):
|
||||
"""Merge all shard parquets into final sarm_progress.parquet."""
|
||||
|
||||
def __init__(self, repo_id, reward_model_path, shard_dir="rabc_shards", push_to_hub=False):
|
||||
super().__init__()
|
||||
self.repo_id = repo_id
|
||||
self.reward_model_path = reward_model_path
|
||||
self.shard_dir = shard_dir
|
||||
self.push_to_hub = push_to_hub
|
||||
|
||||
def run(self, data=None, rank: int = 0, world_size: int = 1):
|
||||
import datetime
|
||||
import logging
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import pandas as pd
|
||||
import pyarrow as pa
|
||||
import pyarrow.parquet as pq
|
||||
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.utils.utils import init_logging
|
||||
|
||||
init_logging()
|
||||
if rank != 0:
|
||||
return
|
||||
|
||||
shard_dir = Path(self.shard_dir)
|
||||
shards = sorted(shard_dir.glob("shard_*.parquet"))
|
||||
if not shards:
|
||||
raise FileNotFoundError(f"No shards found in {shard_dir}")
|
||||
|
||||
# Log shard modification time range to help detect stale files
|
||||
mtimes = [os.path.getmtime(s) for s in shards]
|
||||
oldest = datetime.datetime.fromtimestamp(min(mtimes)).isoformat(timespec="seconds")
|
||||
newest = datetime.datetime.fromtimestamp(max(mtimes)).isoformat(timespec="seconds")
|
||||
logging.info(f"Aggregating {len(shards)} shards (oldest: {oldest}, newest: {newest})")
|
||||
|
||||
df = pd.concat([pd.read_parquet(s) for s in shards], ignore_index=True)
|
||||
df = df.sort_values("index").reset_index(drop=True)
|
||||
|
||||
table = pa.Table.from_pandas(df, preserve_index=False)
|
||||
table = table.replace_schema_metadata({b"reward_model_path": self.reward_model_path.encode()})
|
||||
|
||||
temp_ds = LeRobotDataset(self.repo_id, download_videos=False)
|
||||
out_path = Path(temp_ds.root) / "sarm_progress.parquet"
|
||||
out_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
pq.write_table(table, out_path)
|
||||
logging.info(f"Saved {len(df)} rows to {out_path}")
|
||||
|
||||
for col in ["progress_sparse", "progress_dense"]:
|
||||
if col in df.columns:
|
||||
v = df[col].dropna()
|
||||
logging.info(
|
||||
f"{col}: mean={v.mean():.4f} std={v.std():.4f} min={v.min():.4f} max={v.max():.4f}"
|
||||
)
|
||||
|
||||
if self.push_to_hub:
|
||||
from huggingface_hub import HfApi
|
||||
|
||||
api = HfApi()
|
||||
hub_path = "sarm_progress.parquet"
|
||||
logging.info(f"Uploading to {self.repo_id}/{hub_path}")
|
||||
api.upload_file(
|
||||
path_or_fileobj=str(out_path),
|
||||
path_in_repo=hub_path,
|
||||
repo_id=self.repo_id,
|
||||
repo_type="dataset",
|
||||
)
|
||||
logging.info(f"Uploaded: https://huggingface.co/datasets/{self.repo_id}/blob/main/{hub_path}")
|
||||
|
||||
|
||||
def make_compute_executor(
|
||||
repo_id,
|
||||
reward_model_path,
|
||||
stride,
|
||||
head_mode,
|
||||
device,
|
||||
shard_dir,
|
||||
logs_dir,
|
||||
job_name,
|
||||
slurm,
|
||||
workers,
|
||||
partition,
|
||||
cpus_per_task,
|
||||
mem_per_cpu,
|
||||
):
|
||||
kwargs = {
|
||||
"pipeline": [
|
||||
ComputeProgressShards(repo_id, reward_model_path, stride, head_mode, device, str(shard_dir)),
|
||||
],
|
||||
"logging_dir": str(logs_dir / job_name),
|
||||
}
|
||||
|
||||
if slurm:
|
||||
kwargs.update(
|
||||
{
|
||||
"job_name": job_name,
|
||||
"tasks": workers,
|
||||
"workers": workers,
|
||||
"time": "24:00:00",
|
||||
"partition": partition,
|
||||
"cpus_per_task": cpus_per_task,
|
||||
"sbatch_args": {"mem-per-cpu": mem_per_cpu},
|
||||
}
|
||||
)
|
||||
return SlurmPipelineExecutor(**kwargs)
|
||||
|
||||
kwargs.update({"tasks": workers, "workers": 1})
|
||||
return LocalPipelineExecutor(**kwargs)
|
||||
|
||||
|
||||
def make_aggregate_executor(
|
||||
repo_id,
|
||||
reward_model_path,
|
||||
shard_dir,
|
||||
logs_dir,
|
||||
job_name,
|
||||
slurm,
|
||||
partition,
|
||||
cpus_per_task,
|
||||
mem_per_cpu,
|
||||
push_to_hub,
|
||||
):
|
||||
kwargs = {
|
||||
"pipeline": [
|
||||
AggregateProgress(repo_id, reward_model_path, str(shard_dir), push_to_hub),
|
||||
],
|
||||
"logging_dir": str(logs_dir / job_name),
|
||||
}
|
||||
|
||||
if slurm:
|
||||
kwargs.update(
|
||||
{
|
||||
"job_name": job_name,
|
||||
"tasks": 1,
|
||||
"workers": 1,
|
||||
"time": "02:00:00",
|
||||
"partition": partition,
|
||||
"cpus_per_task": cpus_per_task,
|
||||
"sbatch_args": {"mem-per-cpu": mem_per_cpu},
|
||||
}
|
||||
)
|
||||
return SlurmPipelineExecutor(**kwargs)
|
||||
|
||||
kwargs.update({"tasks": 1, "workers": 1})
|
||||
return LocalPipelineExecutor(**kwargs)
|
||||
|
||||
|
||||
def _add_shared_args(p):
|
||||
p.add_argument(
|
||||
"--repo-id",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Hugging Face repository identifier, e.g. 'user/dataset'.",
|
||||
)
|
||||
p.add_argument(
|
||||
"--shard-dir",
|
||||
type=Path,
|
||||
default=Path("rabc_shards"),
|
||||
help="Directory to read/write per-rank parquet shards.",
|
||||
)
|
||||
p.add_argument(
|
||||
"--logs-dir",
|
||||
type=Path,
|
||||
default=Path("logs"),
|
||||
help="Directory for datatrove logs.",
|
||||
)
|
||||
p.add_argument(
|
||||
"--job-name",
|
||||
type=str,
|
||||
default=None,
|
||||
help="SLURM job name (defaults to rabc_<subcommand>).",
|
||||
)
|
||||
p.add_argument(
|
||||
"--slurm",
|
||||
type=int,
|
||||
default=1,
|
||||
help="1 = submit via SLURM; 0 = run locally (useful for debugging).",
|
||||
)
|
||||
p.add_argument(
|
||||
"--partition",
|
||||
type=str,
|
||||
default=None,
|
||||
help="SLURM partition to submit to.",
|
||||
)
|
||||
p.add_argument(
|
||||
"--cpus-per-task",
|
||||
type=int,
|
||||
default=4,
|
||||
help="Number of CPUs per SLURM task.",
|
||||
)
|
||||
p.add_argument(
|
||||
"--mem-per-cpu",
|
||||
type=str,
|
||||
default="4G",
|
||||
help="Memory per CPU, e.g. '4G' or '1950M'.",
|
||||
)
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="SLURM-distributed SARM RA-BC annotation pipeline",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
)
|
||||
sub = parser.add_subparsers(dest="command", required=True)
|
||||
|
||||
# compute subcommand
|
||||
cp = sub.add_parser(
|
||||
"compute",
|
||||
help="Distribute progress computation across SLURM workers.",
|
||||
)
|
||||
_add_shared_args(cp)
|
||||
cp.add_argument(
|
||||
"--reward-model-path",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path or HF repo id of the SARM reward model.",
|
||||
)
|
||||
cp.add_argument(
|
||||
"--stride",
|
||||
type=int,
|
||||
default=1,
|
||||
help="Compute every Nth frame; intermediate frames are interpolated (must be >= 1).",
|
||||
)
|
||||
cp.add_argument(
|
||||
"--head-mode",
|
||||
type=str,
|
||||
default="sparse",
|
||||
choices=["sparse", "dense", "both"],
|
||||
help="Which reward head(s) to compute.",
|
||||
)
|
||||
cp.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default="cpu",
|
||||
help="Device for reward model inference, e.g. 'cpu' or 'cuda'.",
|
||||
)
|
||||
cp.add_argument(
|
||||
"--workers",
|
||||
type=int,
|
||||
default=50,
|
||||
help="Number of parallel SLURM tasks (one shard per worker).",
|
||||
)
|
||||
|
||||
# aggregate subcommand
|
||||
ap = sub.add_parser(
|
||||
"aggregate",
|
||||
help="Merge per-rank shards into a single sarm_progress.parquet.",
|
||||
)
|
||||
_add_shared_args(ap)
|
||||
ap.add_argument(
|
||||
"--reward-model-path",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path or HF repo id of the SARM reward model (stored in parquet metadata).",
|
||||
)
|
||||
ap.add_argument(
|
||||
"--push-to-hub",
|
||||
action="store_true",
|
||||
help="Upload sarm_progress.parquet to the Hugging Face Hub after aggregation.",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
job_name = args.job_name or f"rabc_{args.command}"
|
||||
kwargs = vars(args)
|
||||
kwargs["slurm"] = kwargs.pop("slurm") == 1
|
||||
kwargs["job_name"] = job_name
|
||||
command = kwargs.pop("command")
|
||||
|
||||
executor = make_compute_executor(**kwargs) if command == "compute" else make_aggregate_executor(**kwargs)
|
||||
|
||||
executor.run()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -14,8 +14,8 @@
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from lerobot.datasets.feature_utils import hw_to_dataset_features
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.datasets.utils import hw_to_dataset_features
|
||||
from lerobot.policies.act.modeling_act import ACTPolicy
|
||||
from lerobot.policies.factory import make_pre_post_processors
|
||||
from lerobot.processor import make_default_processors
|
||||
|
||||
@@ -14,8 +14,8 @@
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from lerobot.datasets.feature_utils import hw_to_dataset_features
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.datasets.utils import hw_to_dataset_features
|
||||
from lerobot.processor import make_default_processors
|
||||
from lerobot.robots.lekiwi.config_lekiwi import LeKiwiClientConfig
|
||||
from lerobot.robots.lekiwi.lekiwi_client import LeKiwiClient
|
||||
|
||||
@@ -16,13 +16,15 @@
|
||||
|
||||
from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
|
||||
from lerobot.configs.types import FeatureType, PolicyFeature
|
||||
from lerobot.datasets.feature_utils import combine_feature_dicts
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
|
||||
from lerobot.datasets.utils import combine_feature_dicts
|
||||
from lerobot.model.kinematics import RobotKinematics
|
||||
from lerobot.policies.act.modeling_act import ACTPolicy
|
||||
from lerobot.policies.factory import make_pre_post_processors
|
||||
from lerobot.processor import (
|
||||
RobotAction,
|
||||
RobotObservation,
|
||||
RobotProcessorPipeline,
|
||||
make_default_teleop_action_processor,
|
||||
)
|
||||
@@ -38,7 +40,6 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
|
||||
InverseKinematicsEEToJoints,
|
||||
)
|
||||
from lerobot.scripts.lerobot_record import record_loop
|
||||
from lerobot.types import RobotAction, RobotObservation
|
||||
from lerobot.utils.control_utils import init_keyboard_listener
|
||||
from lerobot.utils.utils import log_say
|
||||
from lerobot.utils.visualization_utils import init_rerun
|
||||
|
||||
@@ -15,11 +15,11 @@
|
||||
# limitations under the License.
|
||||
|
||||
from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
|
||||
from lerobot.datasets.feature_utils import combine_feature_dicts
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
|
||||
from lerobot.datasets.utils import combine_feature_dicts
|
||||
from lerobot.model.kinematics import RobotKinematics
|
||||
from lerobot.processor import RobotProcessorPipeline
|
||||
from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline
|
||||
from lerobot.processor.converters import (
|
||||
observation_to_transition,
|
||||
robot_action_observation_to_transition,
|
||||
@@ -38,7 +38,6 @@ from lerobot.scripts.lerobot_record import record_loop
|
||||
from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS
|
||||
from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction
|
||||
from lerobot.teleoperators.phone.teleop_phone import Phone
|
||||
from lerobot.types import RobotAction, RobotObservation
|
||||
from lerobot.utils.control_utils import init_keyboard_listener
|
||||
from lerobot.utils.utils import log_say
|
||||
from lerobot.utils.visualization_utils import init_rerun
|
||||
|
||||
@@ -18,7 +18,7 @@ import time
|
||||
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.model.kinematics import RobotKinematics
|
||||
from lerobot.processor import RobotProcessorPipeline
|
||||
from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline
|
||||
from lerobot.processor.converters import (
|
||||
robot_action_observation_to_transition,
|
||||
transition_to_robot_action,
|
||||
@@ -27,7 +27,6 @@ from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
|
||||
from lerobot.robots.so_follower.robot_kinematic_processor import (
|
||||
InverseKinematicsEEToJoints,
|
||||
)
|
||||
from lerobot.types import RobotAction, RobotObservation
|
||||
from lerobot.utils.constants import ACTION
|
||||
from lerobot.utils.robot_utils import precise_sleep
|
||||
from lerobot.utils.utils import log_say
|
||||
|
||||
@@ -16,7 +16,7 @@
|
||||
import time
|
||||
|
||||
from lerobot.model.kinematics import RobotKinematics
|
||||
from lerobot.processor import RobotProcessorPipeline
|
||||
from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline
|
||||
from lerobot.processor.converters import (
|
||||
robot_action_observation_to_transition,
|
||||
transition_to_robot_action,
|
||||
@@ -31,7 +31,6 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
|
||||
from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS
|
||||
from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction
|
||||
from lerobot.teleoperators.phone.teleop_phone import Phone
|
||||
from lerobot.types import RobotAction, RobotObservation
|
||||
from lerobot.utils.robot_utils import precise_sleep
|
||||
from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
|
||||
|
||||
|
||||
@@ -22,8 +22,7 @@ from pathlib import Path
|
||||
import numpy as np
|
||||
import tensorflow_datasets as tfds
|
||||
|
||||
from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
|
||||
from lerobot.utils.utils import get_elapsed_time_in_days_hours_minutes_seconds
|
||||
|
||||
DROID_SHARDS = 2048
|
||||
|
||||
@@ -26,7 +26,7 @@ from huggingface_hub import HfApi
|
||||
from huggingface_hub.constants import REPOCARD_NAME
|
||||
from port_droid import DROID_SHARDS
|
||||
|
||||
from lerobot.datasets.dataset_metadata import CODEBASE_VERSION, LeRobotDatasetMetadata
|
||||
from lerobot.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDatasetMetadata
|
||||
from lerobot.datasets.utils import create_lerobot_dataset_card
|
||||
from lerobot.utils.utils import init_logging
|
||||
|
||||
@@ -155,7 +155,7 @@ class UploadDataset(PipelineStep):
|
||||
from datasets.utils.tqdm import disable_progress_bars
|
||||
from huggingface_hub import CommitOperationAdd, preupload_lfs_files
|
||||
|
||||
from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata
|
||||
from lerobot.utils.utils import init_logging
|
||||
|
||||
init_logging()
|
||||
|
||||
@@ -113,9 +113,8 @@ from lerobot.configs import parser
|
||||
from lerobot.configs.default import DatasetConfig
|
||||
from lerobot.configs.policies import PreTrainedConfig
|
||||
from lerobot.configs.types import RTCAttentionSchedule
|
||||
from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
|
||||
from lerobot.datasets.factory import resolve_delta_timestamps
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
|
||||
from lerobot.policies.factory import get_policy_class, make_pre_post_processors
|
||||
from lerobot.policies.rtc.configuration_rtc import RTCConfig
|
||||
from lerobot.policies.rtc.debug_visualizer import RTCDebugVisualizer
|
||||
|
||||
@@ -78,11 +78,10 @@ from torch import Tensor
|
||||
|
||||
from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig # noqa: F401
|
||||
from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig # noqa: F401
|
||||
from lerobot.cameras.zmq.configuration_zmq import ZMQCameraConfig # noqa: F401
|
||||
from lerobot.configs import parser
|
||||
from lerobot.configs.policies import PreTrainedConfig
|
||||
from lerobot.configs.types import RTCAttentionSchedule
|
||||
from lerobot.datasets.feature_utils import build_dataset_frame, hw_to_dataset_features
|
||||
from lerobot.datasets.utils import build_dataset_frame, hw_to_dataset_features
|
||||
from lerobot.policies.factory import get_policy_class, make_pre_post_processors
|
||||
from lerobot.policies.rtc.action_queue import ActionQueue
|
||||
from lerobot.policies.rtc.configuration_rtc import RTCConfig
|
||||
@@ -98,7 +97,6 @@ from lerobot.robots import ( # noqa: F401
|
||||
bi_so_follower,
|
||||
koch_follower,
|
||||
so_follower,
|
||||
unitree_g1,
|
||||
)
|
||||
from lerobot.robots.utils import make_robot_from_config
|
||||
from lerobot.utils.constants import OBS_IMAGES
|
||||
|
||||
@@ -16,13 +16,15 @@
|
||||
|
||||
from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
|
||||
from lerobot.configs.types import FeatureType, PolicyFeature
|
||||
from lerobot.datasets.feature_utils import combine_feature_dicts
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
|
||||
from lerobot.datasets.utils import combine_feature_dicts
|
||||
from lerobot.model.kinematics import RobotKinematics
|
||||
from lerobot.policies.act.modeling_act import ACTPolicy
|
||||
from lerobot.policies.factory import make_pre_post_processors
|
||||
from lerobot.processor import (
|
||||
RobotAction,
|
||||
RobotObservation,
|
||||
RobotProcessorPipeline,
|
||||
make_default_teleop_action_processor,
|
||||
)
|
||||
@@ -38,7 +40,6 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
|
||||
InverseKinematicsEEToJoints,
|
||||
)
|
||||
from lerobot.scripts.lerobot_record import record_loop
|
||||
from lerobot.types import RobotAction, RobotObservation
|
||||
from lerobot.utils.control_utils import init_keyboard_listener
|
||||
from lerobot.utils.utils import log_say
|
||||
from lerobot.utils.visualization_utils import init_rerun
|
||||
|
||||
@@ -16,11 +16,11 @@
|
||||
|
||||
|
||||
from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
|
||||
from lerobot.datasets.feature_utils import combine_feature_dicts
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
|
||||
from lerobot.datasets.utils import combine_feature_dicts
|
||||
from lerobot.model.kinematics import RobotKinematics
|
||||
from lerobot.processor import RobotProcessorPipeline
|
||||
from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline
|
||||
from lerobot.processor.converters import (
|
||||
observation_to_transition,
|
||||
robot_action_observation_to_transition,
|
||||
@@ -35,7 +35,6 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
|
||||
)
|
||||
from lerobot.scripts.lerobot_record import record_loop
|
||||
from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
|
||||
from lerobot.types import RobotAction, RobotObservation
|
||||
from lerobot.utils.control_utils import init_keyboard_listener
|
||||
from lerobot.utils.utils import log_say
|
||||
from lerobot.utils.visualization_utils import init_rerun
|
||||
|
||||
@@ -19,7 +19,7 @@ import time
|
||||
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.model.kinematics import RobotKinematics
|
||||
from lerobot.processor import RobotProcessorPipeline
|
||||
from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline
|
||||
from lerobot.processor.converters import (
|
||||
robot_action_observation_to_transition,
|
||||
transition_to_robot_action,
|
||||
@@ -28,7 +28,6 @@ from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
|
||||
from lerobot.robots.so_follower.robot_kinematic_processor import (
|
||||
InverseKinematicsEEToJoints,
|
||||
)
|
||||
from lerobot.types import RobotAction, RobotObservation
|
||||
from lerobot.utils.constants import ACTION
|
||||
from lerobot.utils.robot_utils import precise_sleep
|
||||
from lerobot.utils.utils import log_say
|
||||
|
||||
@@ -17,7 +17,7 @@
|
||||
import time
|
||||
|
||||
from lerobot.model.kinematics import RobotKinematics
|
||||
from lerobot.processor import RobotProcessorPipeline
|
||||
from lerobot.processor import RobotAction, RobotObservation, RobotProcessorPipeline
|
||||
from lerobot.processor.converters import (
|
||||
robot_action_observation_to_transition,
|
||||
robot_action_to_transition,
|
||||
@@ -30,7 +30,6 @@ from lerobot.robots.so_follower.robot_kinematic_processor import (
|
||||
InverseKinematicsEEToJoints,
|
||||
)
|
||||
from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
|
||||
from lerobot.types import RobotAction, RobotObservation
|
||||
from lerobot.utils.robot_utils import precise_sleep
|
||||
from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
|
||||
|
||||
|
||||
@@ -19,9 +19,8 @@ from pathlib import Path
|
||||
import torch
|
||||
|
||||
from lerobot.configs.types import FeatureType
|
||||
from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
|
||||
from lerobot.datasets.feature_utils import dataset_to_policy_features
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
|
||||
from lerobot.datasets.utils import dataset_to_policy_features
|
||||
from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
|
||||
from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
|
||||
from lerobot.policies.factory import make_pre_post_processors
|
||||
|
||||
@@ -20,9 +20,9 @@ from pathlib import Path
|
||||
import torch
|
||||
|
||||
from lerobot.configs.types import FeatureType
|
||||
from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
|
||||
from lerobot.datasets.feature_utils import dataset_to_policy_features
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata
|
||||
from lerobot.datasets.streaming_dataset import StreamingLeRobotDataset
|
||||
from lerobot.datasets.utils import dataset_to_policy_features
|
||||
from lerobot.policies.act.configuration_act import ACTConfig
|
||||
from lerobot.policies.act.modeling_act import ACTPolicy
|
||||
from lerobot.policies.factory import make_pre_post_processors
|
||||
|
||||
@@ -5,9 +5,8 @@ from pathlib import Path
|
||||
import torch
|
||||
|
||||
from lerobot.configs.types import FeatureType
|
||||
from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
|
||||
from lerobot.datasets.feature_utils import dataset_to_policy_features
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
|
||||
from lerobot.datasets.utils import dataset_to_policy_features
|
||||
from lerobot.policies.act.configuration_act import ACTConfig
|
||||
from lerobot.policies.act.modeling_act import ACTPolicy
|
||||
from lerobot.policies.factory import make_pre_post_processors
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import torch
|
||||
|
||||
from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
|
||||
from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata
|
||||
from lerobot.policies.act.modeling_act import ACTPolicy
|
||||
from lerobot.policies.factory import make_pre_post_processors
|
||||
from lerobot.policies.utils import build_inference_frame, make_robot_action
|
||||
|
||||
@@ -5,9 +5,8 @@ from pathlib import Path
|
||||
import torch
|
||||
|
||||
from lerobot.configs.types import FeatureType
|
||||
from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
|
||||
from lerobot.datasets.feature_utils import dataset_to_policy_features
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
|
||||
from lerobot.datasets.utils import dataset_to_policy_features
|
||||
from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
|
||||
from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
|
||||
from lerobot.policies.factory import make_pre_post_processors
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import torch
|
||||
|
||||
from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
|
||||
from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata
|
||||
from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
|
||||
from lerobot.policies.factory import make_pre_post_processors
|
||||
from lerobot.policies.utils import build_inference_frame, make_robot_action
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import torch
|
||||
|
||||
from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
|
||||
from lerobot.datasets.feature_utils import hw_to_dataset_features
|
||||
from lerobot.datasets.utils import hw_to_dataset_features
|
||||
from lerobot.policies.factory import make_pre_post_processors
|
||||
from lerobot.policies.pi0.modeling_pi0 import PI0Policy
|
||||
from lerobot.policies.utils import build_inference_frame, make_robot_action
|
||||
|
||||
@@ -6,8 +6,8 @@ from queue import Empty, Full
|
||||
import torch
|
||||
import torch.optim as optim
|
||||
|
||||
from lerobot.datasets.feature_utils import hw_to_dataset_features
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.datasets.utils import hw_to_dataset_features
|
||||
from lerobot.envs.configs import HILSerlProcessorConfig, HILSerlRobotEnvConfig
|
||||
from lerobot.policies.sac.configuration_sac import SACConfig
|
||||
from lerobot.policies.sac.modeling_sac import SACPolicy
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import torch
|
||||
|
||||
from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
|
||||
from lerobot.datasets.feature_utils import hw_to_dataset_features
|
||||
from lerobot.datasets.utils import hw_to_dataset_features
|
||||
from lerobot.policies.factory import make_pre_post_processors
|
||||
from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
|
||||
from lerobot.policies.utils import build_inference_frame, make_robot_action
|
||||
|
||||
@@ -14,20 +14,20 @@
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import time
|
||||
from collections import deque
|
||||
|
||||
import numpy as np
|
||||
import onnxruntime as ort
|
||||
from huggingface_hub import hf_hub_download
|
||||
|
||||
from lerobot.robots.unitree_g1.g1_utils import (
|
||||
REMOTE_AXES,
|
||||
REMOTE_BUTTONS,
|
||||
G1_29_JointIndex,
|
||||
get_gravity_orientation,
|
||||
)
|
||||
from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config
|
||||
from lerobot.robots.unitree_g1.g1_utils import G1_29_JointIndex
|
||||
from lerobot.robots.unitree_g1.unitree_g1 import UnitreeG1
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@@ -36,13 +36,18 @@ GROOT_DEFAULT_ANGLES[[0, 6]] = -0.1 # Hip pitch
|
||||
GROOT_DEFAULT_ANGLES[[3, 9]] = 0.3 # Knee
|
||||
GROOT_DEFAULT_ANGLES[[4, 10]] = -0.2 # Ankle pitch
|
||||
|
||||
MISSING_JOINTS = []
|
||||
G1_MODEL = "g1_23" # Or "g1_29"
|
||||
if G1_MODEL == "g1_23":
|
||||
MISSING_JOINTS = [12, 14, 20, 21, 27, 28] # Waist yaw/pitch, wrist pitch/yaw
|
||||
|
||||
# Control parameters
|
||||
ACTION_SCALE = 0.25
|
||||
CONTROL_DT = 0.02 # 50Hz
|
||||
ANG_VEL_SCALE: float = 0.25
|
||||
DOF_POS_SCALE: float = 1.0
|
||||
DOF_VEL_SCALE: float = 0.05
|
||||
CMD_SCALE: list[float] = [2.0, 2.0, 0.25]
|
||||
CMD_SCALE: list = [2.0, 2.0, 0.25]
|
||||
|
||||
|
||||
DEFAULT_GROOT_REPO_ID = "nepyope/GR00T-WholeBodyControl_g1"
|
||||
@@ -80,11 +85,11 @@ def load_groot_policies(
|
||||
class GrootLocomotionController:
|
||||
"""GR00T lower-body locomotion controller for the Unitree G1."""
|
||||
|
||||
control_dt = CONTROL_DT # Expose for unitree_g1.py
|
||||
|
||||
def __init__(self):
|
||||
# Load policies
|
||||
self.policy_balance, self.policy_walk = load_groot_policies()
|
||||
def __init__(self, policy_balance, policy_walk, robot, config):
|
||||
self.policy_balance = policy_balance
|
||||
self.policy_walk = policy_walk
|
||||
self.robot = robot
|
||||
self.config = config
|
||||
|
||||
self.cmd = np.array([0.0, 0.0, 0.0], dtype=np.float32) # vx, vy, theta_dot
|
||||
|
||||
@@ -104,60 +109,45 @@ class GrootLocomotionController:
|
||||
|
||||
logger.info("GrootLocomotionController initialized")
|
||||
|
||||
def reset(self) -> None:
|
||||
"""Reset internal state for a new episode."""
|
||||
self.cmd[:] = 0.0
|
||||
self.groot_qj_all[:] = 0.0
|
||||
self.groot_dqj_all[:] = 0.0
|
||||
self.groot_action[:] = 0.0
|
||||
self.groot_obs_single[:] = 0.0
|
||||
self.groot_obs_stacked[:] = 0.0
|
||||
self.groot_height_cmd = 0.74
|
||||
self.groot_orientation_cmd[:] = 0.0
|
||||
self.groot_obs_history.clear()
|
||||
for _ in range(6):
|
||||
self.groot_obs_history.append(np.zeros(86, dtype=np.float32))
|
||||
def run_step(self):
|
||||
# Get current observation
|
||||
obs = self.robot.get_observation()
|
||||
|
||||
def run_step(self, action: dict, lowstate) -> dict:
|
||||
"""Run one step of the locomotion controller.
|
||||
if not obs:
|
||||
return
|
||||
|
||||
Args:
|
||||
action: Action dict containing remote.lx/ly/rx/ry and buttons
|
||||
lowstate: Robot lowstate containing motor positions/velocities and IMU
|
||||
|
||||
Returns:
|
||||
Action dict for lower body joints (0-14)
|
||||
"""
|
||||
if lowstate is None:
|
||||
return {}
|
||||
|
||||
buttons = [int(action.get(k, 0)) for k in REMOTE_BUTTONS]
|
||||
if buttons[0]: # R1 - raise waist
|
||||
# Get command from remote controller
|
||||
if obs["remote.buttons"][0]: # R1 - raise waist
|
||||
self.groot_height_cmd += 0.001
|
||||
self.groot_height_cmd = np.clip(self.groot_height_cmd, 0.50, 1.00)
|
||||
if buttons[4]: # R2 - lower waist
|
||||
if obs["remote.buttons"][4]: # R2 - lower waist
|
||||
self.groot_height_cmd -= 0.001
|
||||
self.groot_height_cmd = np.clip(self.groot_height_cmd, 0.50, 1.00)
|
||||
|
||||
lx, ly, rx, _ry = (action.get(k, 0.0) for k in REMOTE_AXES)
|
||||
self.cmd[0] = ly # Forward/backward
|
||||
self.cmd[1] = -lx # Left/right (negated)
|
||||
self.cmd[2] = -rx # Rotation rate (negated)
|
||||
self.cmd[0] = obs["remote.ly"] # Forward/backward
|
||||
self.cmd[1] = obs["remote.lx"] * -1 # Left/right
|
||||
self.cmd[2] = obs["remote.rx"] * -1 # Rotation rate
|
||||
|
||||
# Get joint positions and velocities from lowstate
|
||||
# Get joint positions and velocities from flat dict
|
||||
for motor in G1_29_JointIndex:
|
||||
name = motor.name
|
||||
idx = motor.value
|
||||
self.groot_qj_all[idx] = lowstate.motor_state[idx].q
|
||||
self.groot_dqj_all[idx] = lowstate.motor_state[idx].dq
|
||||
self.groot_qj_all[idx] = obs[f"{name}.q"]
|
||||
self.groot_dqj_all[idx] = obs[f"{name}.dq"]
|
||||
|
||||
# Adapt observation for g1_23dof
|
||||
for idx in MISSING_JOINTS:
|
||||
self.groot_qj_all[idx] = 0.0
|
||||
self.groot_dqj_all[idx] = 0.0
|
||||
|
||||
# Scale joint positions and velocities
|
||||
qj_obs = self.groot_qj_all.copy()
|
||||
dqj_obs = self.groot_dqj_all.copy()
|
||||
|
||||
# Express IMU data in gravity frame of reference
|
||||
quat = lowstate.imu_state.quaternion
|
||||
ang_vel = np.array(lowstate.imu_state.gyroscope, dtype=np.float32)
|
||||
gravity_orientation = get_gravity_orientation(quat)
|
||||
quat = [obs["imu.quat.w"], obs["imu.quat.x"], obs["imu.quat.y"], obs["imu.quat.z"]]
|
||||
ang_vel = np.array([obs["imu.gyro.x"], obs["imu.gyro.y"], obs["imu.gyro.z"]], dtype=np.float32)
|
||||
gravity_orientation = self.robot.get_gravity_orientation(quat)
|
||||
|
||||
# Scale joint positions and velocities before policy inference
|
||||
qj_obs = (qj_obs - GROOT_DEFAULT_ANGLES) * DOF_POS_SCALE
|
||||
@@ -196,10 +186,73 @@ class GrootLocomotionController:
|
||||
# Transform action back to target joint positions
|
||||
target_dof_pos_15 = GROOT_DEFAULT_ANGLES[:15] + self.groot_action * ACTION_SCALE
|
||||
|
||||
# Build action dict
|
||||
# Build action dict (only first 15 joints for GR00T)
|
||||
action_dict = {}
|
||||
for i in range(15):
|
||||
motor_name = G1_29_JointIndex(i).name
|
||||
action_dict[f"{motor_name}.q"] = float(target_dof_pos_15[i])
|
||||
|
||||
return action_dict
|
||||
# Zero out missing joints for g1_23dof
|
||||
for joint_idx in MISSING_JOINTS:
|
||||
motor_name = G1_29_JointIndex(joint_idx).name
|
||||
action_dict[f"{motor_name}.q"] = 0.0
|
||||
|
||||
# Send action to robot
|
||||
self.robot.send_action(action_dict)
|
||||
|
||||
|
||||
def run(repo_id: str = DEFAULT_GROOT_REPO_ID) -> None:
|
||||
"""Main function to run the GR00T locomotion controller.
|
||||
|
||||
Args:
|
||||
repo_id: Hugging Face Hub repository ID for GR00T policies.
|
||||
"""
|
||||
# Load policies
|
||||
policy_balance, policy_walk = load_groot_policies(repo_id=repo_id)
|
||||
|
||||
# Initialize robot
|
||||
config = UnitreeG1Config()
|
||||
robot = UnitreeG1(config)
|
||||
|
||||
robot.connect()
|
||||
|
||||
# Initialize gr00T locomotion controller
|
||||
groot_controller = GrootLocomotionController(
|
||||
policy_balance=policy_balance,
|
||||
policy_walk=policy_walk,
|
||||
robot=robot,
|
||||
config=config,
|
||||
)
|
||||
|
||||
try:
|
||||
robot.reset(CONTROL_DT, GROOT_DEFAULT_ANGLES)
|
||||
|
||||
logger.info("Use joystick: LY=fwd/back, LX=left/right, RX=rotate, R1=raise waist, R2=lower waist")
|
||||
logger.info("Press Ctrl+C to stop")
|
||||
|
||||
# Run step
|
||||
while not robot._shutdown_event.is_set():
|
||||
start_time = time.time()
|
||||
groot_controller.run_step()
|
||||
elapsed = time.time() - start_time
|
||||
sleep_time = max(0, CONTROL_DT - elapsed)
|
||||
time.sleep(sleep_time)
|
||||
except KeyboardInterrupt:
|
||||
logger.info("Stopping locomotion...")
|
||||
finally:
|
||||
if robot.is_connected:
|
||||
robot.disconnect()
|
||||
logger.info("Done!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="GR00T Locomotion Controller for Unitree G1")
|
||||
parser.add_argument(
|
||||
"--repo-id",
|
||||
type=str,
|
||||
default=DEFAULT_GROOT_REPO_ID,
|
||||
help=f"Hugging Face Hub repo ID for GR00T policies (default: {DEFAULT_GROOT_REPO_ID})",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
run(repo_id=args.repo_id)
|
||||
@@ -14,21 +14,21 @@
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import logging
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import onnx
|
||||
import onnxruntime as ort
|
||||
from huggingface_hub import hf_hub_download
|
||||
|
||||
from lerobot.robots.unitree_g1.g1_utils import (
|
||||
REMOTE_AXES,
|
||||
G1_29_JointArmIndex,
|
||||
G1_29_JointIndex,
|
||||
get_gravity_orientation,
|
||||
)
|
||||
from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config
|
||||
from lerobot.robots.unitree_g1.g1_utils import G1_29_JointIndex
|
||||
from lerobot.robots.unitree_g1.unitree_g1 import UnitreeG1
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
DEFAULT_ANGLES = np.zeros(29, dtype=np.float32)
|
||||
@@ -40,13 +40,18 @@ DEFAULT_ANGLES[16] = 0.2 # Left shoulder roll
|
||||
DEFAULT_ANGLES[23] = -0.2 # Right shoulder roll
|
||||
DEFAULT_ANGLES[[18, 25]] = 0.6 # Elbow
|
||||
|
||||
MISSING_JOINTS = []
|
||||
G1_MODEL = "g1_23" # Or "g1_29"
|
||||
if G1_MODEL == "g1_23":
|
||||
MISSING_JOINTS = [12, 14, 20, 21, 27, 28] # Waist yaw/pitch, wrist pitch/yaw
|
||||
|
||||
# Control parameters
|
||||
ACTION_SCALE = 0.25
|
||||
CONTROL_DT = 0.005 # 200Hz
|
||||
CONTROL_DT = 0.02 # 50Hz
|
||||
ANG_VEL_SCALE = 0.25
|
||||
DOF_POS_SCALE = 1.0
|
||||
DOF_VEL_SCALE = 0.05
|
||||
GAIT_PERIOD = 0.5
|
||||
GAIT_PERIOD = 1.0
|
||||
|
||||
|
||||
DEFAULT_HOLOSOMA_REPO_ID = "nepyope/holosoma_locomotion"
|
||||
@@ -82,7 +87,7 @@ def load_policy(
|
||||
logger.info(f"Policy loaded: {policy.get_inputs()[0].shape} → {policy.get_outputs()[0].shape}")
|
||||
|
||||
# Extract KP/KD from ONNX metadata
|
||||
model = onnx.load(policy_path, load_external_data=False)
|
||||
model = onnx.load(policy_path)
|
||||
metadata = {prop.key: prop.value for prop in model.metadata_props}
|
||||
|
||||
if "kp" not in metadata or "kd" not in metadata:
|
||||
@@ -96,13 +101,15 @@ def load_policy(
|
||||
|
||||
|
||||
class HolosomaLocomotionController:
|
||||
"""Holosoma lower-body locomotion controller for Unitree G1."""
|
||||
"""Holosoma whole-body locomotion controller for Unitree G1."""
|
||||
|
||||
control_dt = CONTROL_DT # Expose for unitree_g1.py
|
||||
def __init__(self, policy, robot, kp: np.ndarray, kd: np.ndarray):
|
||||
self.policy = policy
|
||||
self.robot = robot
|
||||
|
||||
def __init__(self):
|
||||
# Load policy and gains
|
||||
self.policy, self.kp, self.kd = load_policy()
|
||||
# Override robot's PD gains with policy gains
|
||||
self.robot.kp = kp
|
||||
self.robot.kd = kd
|
||||
|
||||
self.cmd = np.zeros(3, dtype=np.float32)
|
||||
|
||||
@@ -117,55 +124,35 @@ class HolosomaLocomotionController:
|
||||
self.phase_dt = 2 * np.pi / ((1.0 / CONTROL_DT) * GAIT_PERIOD)
|
||||
self.is_standing = True
|
||||
|
||||
logger.info("HolosomaLocomotionController initialized")
|
||||
def run_step(self):
|
||||
# Get current observation
|
||||
obs = self.robot.get_observation()
|
||||
|
||||
def reset(self) -> None:
|
||||
"""Reset internal state for a new episode."""
|
||||
self.cmd[:] = 0.0
|
||||
self.qj[:] = 0.0
|
||||
self.dqj[:] = 0.0
|
||||
self.obs[:] = 0.0
|
||||
self.last_action[:] = 0.0
|
||||
self.phase = np.array([[0.0, np.pi]], dtype=np.float32)
|
||||
self.is_standing = True
|
||||
if not obs:
|
||||
return
|
||||
|
||||
def run_step(self, action: dict, lowstate) -> dict:
|
||||
"""Run one step of the locomotion controller.
|
||||
|
||||
Args:
|
||||
action: Action dict containing remote.lx/ly/rx/ry
|
||||
lowstate: Robot lowstate containing motor positions/velocities and IMU
|
||||
|
||||
Returns:
|
||||
Action dict for lower body joints (0-14)
|
||||
"""
|
||||
if lowstate is None:
|
||||
return {}
|
||||
|
||||
lx, ly, rx, _ry = (action.get(k, 0.0) for k in REMOTE_AXES)
|
||||
ly = ly if abs(ly) > 0.1 else 0.0
|
||||
lx = lx if abs(lx) > 0.1 else 0.0
|
||||
rx = rx if abs(rx) > 0.1 else 0.0
|
||||
ly = np.clip(ly, -0.3, 0.3)
|
||||
lx = np.clip(lx, -0.3, 0.3)
|
||||
# Get command from remote controller
|
||||
ly = obs["remote.ly"] if abs(obs["remote.ly"]) > 0.1 else 0.0
|
||||
lx = obs["remote.lx"] if abs(obs["remote.lx"]) > 0.1 else 0.0
|
||||
rx = obs["remote.rx"] if abs(obs["remote.rx"]) > 0.1 else 0.0
|
||||
self.cmd[:] = [ly, -lx, -rx]
|
||||
|
||||
# Get joint positions and velocities from lowstate
|
||||
# Get joint positions and velocities
|
||||
for motor in G1_29_JointIndex:
|
||||
name = motor.name
|
||||
idx = motor.value
|
||||
self.qj[idx] = lowstate.motor_state[idx].q
|
||||
self.dqj[idx] = lowstate.motor_state[idx].dq
|
||||
self.qj[idx] = obs[f"{name}.q"]
|
||||
self.dqj[idx] = obs[f"{name}.dq"]
|
||||
|
||||
# Hide arm positions from policy (show DEFAULT_ANGLES instead)
|
||||
# This prevents policy from reacting to teleop arm movements
|
||||
for arm_joint in G1_29_JointArmIndex:
|
||||
self.qj[arm_joint.value] = DEFAULT_ANGLES[arm_joint.value]
|
||||
self.dqj[arm_joint.value] = 0.0
|
||||
# Adapt observation for g1_23dof
|
||||
for idx in MISSING_JOINTS:
|
||||
self.qj[idx] = 0.0
|
||||
self.dqj[idx] = 0.0
|
||||
|
||||
# Express IMU data in gravity frame of reference
|
||||
quat = lowstate.imu_state.quaternion
|
||||
ang_vel = np.array(lowstate.imu_state.gyroscope, dtype=np.float32)
|
||||
gravity = get_gravity_orientation(quat)
|
||||
quat = [obs["imu.quat.w"], obs["imu.quat.x"], obs["imu.quat.y"], obs["imu.quat.z"]]
|
||||
ang_vel = np.array([obs["imu.gyro.x"], obs["imu.gyro.y"], obs["imu.gyro.z"]], dtype=np.float32)
|
||||
gravity = self.robot.get_gravity_orientation(quat)
|
||||
|
||||
# Scale joint positions and velocities before policy inference
|
||||
qj_obs = (self.qj - DEFAULT_ANGLES) * DOF_POS_SCALE
|
||||
@@ -199,16 +186,79 @@ class HolosomaLocomotionController:
|
||||
# Run policy inference
|
||||
ort_in = {self.policy.get_inputs()[0].name: self.obs.reshape(1, -1).astype(np.float32)}
|
||||
raw_action = self.policy.run(None, ort_in)[0].squeeze()
|
||||
policy_action = np.clip(raw_action, -100.0, 100.0)
|
||||
self.last_action = policy_action.copy()
|
||||
action = np.clip(raw_action, -100.0, 100.0)
|
||||
self.last_action = action.copy()
|
||||
|
||||
# Transform action back to target joint positions
|
||||
target = DEFAULT_ANGLES + policy_action * ACTION_SCALE
|
||||
target = DEFAULT_ANGLES + action * ACTION_SCALE
|
||||
|
||||
# Build action dict (first 15 joints only)
|
||||
# Build action dict
|
||||
action_dict = {}
|
||||
for i in range(15):
|
||||
motor_name = G1_29_JointIndex(i).name
|
||||
action_dict[f"{motor_name}.q"] = float(target[i])
|
||||
for motor in G1_29_JointIndex:
|
||||
action_dict[f"{motor.name}.q"] = float(target[motor.value])
|
||||
|
||||
return action_dict
|
||||
# Zero out missing joints for g1_23dof
|
||||
for joint_idx in MISSING_JOINTS:
|
||||
motor_name = G1_29_JointIndex(joint_idx).name
|
||||
action_dict[f"{motor_name}.q"] = 0.0
|
||||
|
||||
# Send action to robot
|
||||
self.robot.send_action(action_dict)
|
||||
|
||||
|
||||
def run(repo_id: str = DEFAULT_HOLOSOMA_REPO_ID, policy_type: str = "fastsac") -> None:
|
||||
"""Main function to run the Holosoma locomotion controller.
|
||||
|
||||
Args:
|
||||
repo_id: Hugging Face Hub repository ID for Holosoma policies.
|
||||
policy_type: Policy type to use ('fastsac' or 'ppo').
|
||||
"""
|
||||
# Load policy and gains
|
||||
policy, kp, kd = load_policy(repo_id=repo_id, policy_type=policy_type)
|
||||
|
||||
# Initialize robot
|
||||
config = UnitreeG1Config()
|
||||
robot = UnitreeG1(config)
|
||||
robot.connect()
|
||||
|
||||
holosoma_controller = HolosomaLocomotionController(policy, robot, kp, kd)
|
||||
|
||||
try:
|
||||
robot.reset(CONTROL_DT, DEFAULT_ANGLES)
|
||||
|
||||
logger.info("Use joystick: LY=fwd/back, LX=left/right, RX=rotate")
|
||||
logger.info("Press Ctrl+C to stop")
|
||||
|
||||
# Run step
|
||||
while not robot._shutdown_event.is_set():
|
||||
start_time = time.time()
|
||||
holosoma_controller.run_step()
|
||||
elapsed = time.time() - start_time
|
||||
sleep_time = max(0, CONTROL_DT - elapsed)
|
||||
time.sleep(sleep_time)
|
||||
except KeyboardInterrupt:
|
||||
logger.info("Stopping locomotion...")
|
||||
finally:
|
||||
if robot.is_connected:
|
||||
robot.disconnect()
|
||||
logger.info("Done!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="Holosoma Locomotion Controller for Unitree G1")
|
||||
parser.add_argument(
|
||||
"--repo-id",
|
||||
type=str,
|
||||
default=DEFAULT_HOLOSOMA_REPO_ID,
|
||||
help=f"Hugging Face Hub repo ID for Holosoma policies (default: {DEFAULT_HOLOSOMA_REPO_ID})",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--policy",
|
||||
type=str,
|
||||
choices=["fastsac", "ppo"],
|
||||
default="fastsac",
|
||||
help="Policy type to use: 'fastsac' (default) or 'ppo'",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
run(repo_id=args.repo_id, policy_type=args.policy)
|
||||
@@ -25,11 +25,11 @@ discord = "https://discord.gg/s3KuuzsPFb"
|
||||
|
||||
[project]
|
||||
name = "lerobot"
|
||||
version = "0.5.1"
|
||||
version = "0.4.5"
|
||||
description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
|
||||
dynamic = ["readme"]
|
||||
license = { text = "Apache-2.0" }
|
||||
requires-python = ">=3.12"
|
||||
requires-python = ">=3.10"
|
||||
authors = [
|
||||
{ name = "Rémi Cadène", email = "re.cadene@gmail.com" },
|
||||
{ name = "Simon Alibert", email = "alibert.sim@gmail.com" },
|
||||
@@ -50,8 +50,7 @@ classifiers = [
|
||||
"Intended Audience :: Education",
|
||||
"Intended Audience :: Science/Research",
|
||||
"License :: OSI Approved :: Apache Software License",
|
||||
"Programming Language :: Python :: 3.12",
|
||||
"Programming Language :: Python :: 3.13",
|
||||
"Programming Language :: Python :: 3.10",
|
||||
"Topic :: Software Development :: Build Tools",
|
||||
"Topic :: Scientific/Engineering :: Artificial Intelligence",
|
||||
]
|
||||
@@ -62,28 +61,26 @@ dependencies = [
|
||||
# Hugging Face dependencies
|
||||
"datasets>=4.0.0,<5.0.0",
|
||||
"diffusers>=0.27.2,<0.36.0",
|
||||
"huggingface-hub>=1.0.0,<2.0.0",
|
||||
"huggingface-hub[cli]>=1.0.0,<2.0.0",
|
||||
"accelerate>=1.10.0,<2.0.0",
|
||||
|
||||
# Core dependencies
|
||||
"numpy>=2.0.0,<2.3.0", # NOTE: Explicitly listing numpy helps the resolver converge faster. Upper bound imposed by opencv-python-headless.
|
||||
"setuptools>=71.0.0,<81.0.0",
|
||||
"cmake>=3.29.0.1,<4.2.0",
|
||||
"packaging>=24.2,<26.0",
|
||||
|
||||
"torch>=2.2.1,<2.11.0",
|
||||
"torchcodec>=0.2.1,<0.11.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')",
|
||||
"torchvision>=0.21.0,<0.26.0",
|
||||
|
||||
"einops>=0.8.0,<0.9.0",
|
||||
"opencv-python-headless>=4.9.0,<4.14.0",
|
||||
"opencv-python-headless>=4.9.0,<4.13.0",
|
||||
"av>=15.0.0,<16.0.0",
|
||||
"jsonlines>=4.0.0,<5.0.0",
|
||||
"pynput>=1.7.8,<1.9.0",
|
||||
"packaging>=24.2,<26.0",
|
||||
"pynput>=1.7.7,<1.9.0",
|
||||
"pyserial>=3.5,<4.0",
|
||||
|
||||
"wandb>=0.24.0,<0.25.0",
|
||||
"draccus==0.10.0", # TODO: Relax version constraint
|
||||
|
||||
"torch>=2.2.1,<2.11.0", # TODO: Bump dependency
|
||||
"torchcodec>=0.2.1,<0.11.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')", # TODO: Bump dependency
|
||||
"torchvision>=0.21.0,<0.26.0", # TODO: Bump dependency
|
||||
|
||||
"draccus==0.10.0", # TODO: Remove ==
|
||||
"gymnasium>=1.1.1,<2.0.0",
|
||||
"rerun-sdk>=0.24.0,<0.27.0",
|
||||
|
||||
@@ -98,14 +95,13 @@ dependencies = [
|
||||
|
||||
# Common
|
||||
pygame-dep = ["pygame>=2.5.1,<2.7.0"]
|
||||
placo-dep = ["placo>=0.9.6,<0.9.17"]
|
||||
placo-dep = ["placo>=0.9.6,<0.10.0"]
|
||||
transformers-dep = ["transformers>=5.3.0,<6.0.0"]
|
||||
grpcio-dep = ["grpcio==1.73.1", "protobuf>=6.31.1,<6.32.0"]
|
||||
can-dep = ["python-can>=4.2.0,<5.0.0"]
|
||||
peft-dep = ["peft>=0.18.0,<1.0.0"]
|
||||
scipy-dep = ["scipy>=1.14.0,<2.0.0"]
|
||||
qwen-vl-utils-dep = ["qwen-vl-utils>=0.0.11,<0.1.0"]
|
||||
matplotlib-dep = ["matplotlib>=3.10.3,<4.0.0", "contourpy>=1.3.0,<2.0.0"] # NOTE: Explicitly listing contourpy helps the resolver converge faster.
|
||||
|
||||
# Motors
|
||||
feetech = ["feetech-servo-sdk>=1.0.0,<2.0.0"]
|
||||
@@ -119,21 +115,20 @@ gamepad = ["lerobot[pygame-dep]", "hidapi>=0.14.0,<0.15.0"]
|
||||
hopejr = ["lerobot[feetech]", "lerobot[pygame-dep]"]
|
||||
lekiwi = ["lerobot[feetech]", "pyzmq>=26.2.1,<28.0.0"]
|
||||
unitree_g1 = [
|
||||
# "unitree-sdk2==1.0.1",
|
||||
"pyzmq>=26.2.1,<28.0.0",
|
||||
"onnxruntime>=1.16.0,<2.0.0",
|
||||
"onnx>=1.16.0,<2.0.0",
|
||||
"pin>=3.0.0,<4.0.0",
|
||||
"meshcat>=0.3.0,<0.4.0",
|
||||
"lerobot[matplotlib-dep]",
|
||||
"lerobot[pygame-dep]",
|
||||
"matplotlib>=3.9.0,<4.0.0",
|
||||
"casadi>=3.6.0,<4.0.0",
|
||||
]
|
||||
reachy2 = ["reachy2_sdk>=1.0.15,<1.1.0"]
|
||||
kinematics = ["lerobot[placo-dep]"]
|
||||
intelrealsense = [
|
||||
"pyrealsense2>=2.55.1.6486,<2.57.0 ; sys_platform != 'darwin'",
|
||||
"pyrealsense2-macosx>=2.54,<2.57.0 ; sys_platform == 'darwin'",
|
||||
"pyrealsense2-macosx>=2.54,<2.55.0 ; sys_platform == 'darwin'",
|
||||
]
|
||||
phone = ["hebi-py>=2.8.0,<2.12.0", "teleop>=0.1.0,<0.2.0", "fastapi<1.0", "lerobot[scipy-dep]"]
|
||||
phone = ["hebi-py>=2.8.0,<2.12.0", "teleop>=0.1.0,<0.2.0", "fastapi<1.0"]
|
||||
|
||||
# Policies
|
||||
wallx = [
|
||||
@@ -156,12 +151,12 @@ groot = [
|
||||
"ninja>=1.11.1,<2.0.0",
|
||||
"flash-attn>=2.5.9,<3.0.0 ; sys_platform != 'darwin'"
|
||||
]
|
||||
sarm = ["lerobot[transformers-dep]", "faker>=33.0.0,<35.0.0", "lerobot[matplotlib-dep]", "lerobot[qwen-vl-utils-dep]"]
|
||||
sarm = ["lerobot[transformers-dep]", "faker>=33.0.0,<35.0.0", "matplotlib>=3.10.3,<4.0.0", "lerobot[qwen-vl-utils-dep]"]
|
||||
xvla = ["lerobot[transformers-dep]"]
|
||||
hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.13,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]
|
||||
|
||||
# Features
|
||||
async = ["lerobot[grpcio-dep]", "lerobot[matplotlib-dep]"]
|
||||
async = ["lerobot[grpcio-dep]", "matplotlib>=3.10.3,<4.0.0"]
|
||||
peft = ["lerobot[transformers-dep]", "lerobot[peft-dep]"]
|
||||
|
||||
# Development
|
||||
@@ -170,19 +165,13 @@ test = ["pytest>=8.1.0,<9.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0
|
||||
video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]
|
||||
|
||||
# Simulation
|
||||
# NOTE: Explicitly listing scipy helps flatten the dependecy tree.
|
||||
aloha = ["gym-aloha>=0.1.2,<0.2.0", "lerobot[scipy-dep]"]
|
||||
aloha = ["gym-aloha>=0.1.2,<0.2.0"]
|
||||
pusht = ["gym-pusht>=0.1.5,<0.2.0", "pymunk>=6.6.0,<7.0.0"] # TODO: Fix pymunk version in gym-pusht instead
|
||||
libero = ["lerobot[transformers-dep]", "hf-libero>=0.1.3,<0.2.0; sys_platform == 'linux'", "lerobot[scipy-dep]"]
|
||||
metaworld = ["metaworld==3.0.0", "lerobot[scipy-dep]"]
|
||||
libero = ["lerobot[transformers-dep]", "hf-libero>=0.1.3,<0.2.0"]
|
||||
metaworld = ["metaworld==3.0.0"]
|
||||
|
||||
# All
|
||||
all = [
|
||||
# NOTE(resolver hint): scipy is pulled in transitively via lerobot[scipy-dep] through
|
||||
# multiple extras (aloha, metaworld, pi, wallx, phone). Listing it explicitly
|
||||
# helps pip's resolver converge by constraining scipy early, before it encounters
|
||||
# the loose scipy requirements from transitive deps like dm-control and metaworld.
|
||||
"scipy>=1.14.0,<2.0.0",
|
||||
"lerobot[dynamixel]",
|
||||
"lerobot[gamepad]",
|
||||
"lerobot[hopejr]",
|
||||
@@ -203,11 +192,10 @@ all = [
|
||||
"lerobot[aloha]",
|
||||
"lerobot[pusht]",
|
||||
"lerobot[phone]",
|
||||
"lerobot[libero]; sys_platform == 'linux'",
|
||||
"lerobot[libero]",
|
||||
"lerobot[metaworld]",
|
||||
"lerobot[sarm]",
|
||||
"lerobot[peft]",
|
||||
# "lerobot[unitree_g1]", TODO: Unitree requires specific installation instructions for unitree_sdk2
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
@@ -236,7 +224,7 @@ lerobot = ["envs/*.json"]
|
||||
where = ["src"]
|
||||
|
||||
[tool.ruff]
|
||||
target-version = "py312"
|
||||
target-version = "py310"
|
||||
line-length = 110
|
||||
exclude = ["tests/artifacts/**/*.safetensors", "*_pb2.py", "*_pb2_grpc.py"]
|
||||
|
||||
@@ -328,7 +316,7 @@ default.extend-ignore-identifiers-re = [
|
||||
# Uncomment [tool.mypy] first, then uncomment individual module overrides as they get proper type annotations
|
||||
|
||||
[tool.mypy]
|
||||
python_version = "3.12"
|
||||
python_version = "3.10"
|
||||
ignore_missing_imports = true
|
||||
follow_imports = "skip"
|
||||
# warn_return_any = true
|
||||
|
||||
@@ -1,73 +1,76 @@
|
||||
#
|
||||
# This file is autogenerated by pip-compile with Python 3.12
|
||||
# This file is autogenerated by pip-compile with Python 3.10
|
||||
# by the following command:
|
||||
#
|
||||
# pip-compile --output-file=requirements-macos.txt requirements.in
|
||||
#
|
||||
-e .[all]
|
||||
# via -[all]
|
||||
absl-py==2.4.0
|
||||
absl-py==2.3.1
|
||||
# via
|
||||
# dm-control
|
||||
# dm-env
|
||||
# dm-tree
|
||||
# labmaze
|
||||
# mujoco
|
||||
accelerate==1.13.0
|
||||
# tensorboard
|
||||
accelerate==1.11.0
|
||||
# via
|
||||
# lerobot
|
||||
# peft
|
||||
aiohappyeyeballs==2.6.1
|
||||
# via aiohttp
|
||||
aiohttp==3.13.3
|
||||
aiohttp==3.13.1
|
||||
# via fsspec
|
||||
aiosignal==1.4.0
|
||||
# via aiohttp
|
||||
annotated-doc==0.0.4
|
||||
# via
|
||||
# fastapi
|
||||
# typer
|
||||
annotated-types==0.7.0
|
||||
# via pydantic
|
||||
anyio==4.12.1
|
||||
antlr4-python3-runtime==4.9.3
|
||||
# via
|
||||
# hydra-core
|
||||
# omegaconf
|
||||
anyio==4.11.0
|
||||
# via
|
||||
# httpx
|
||||
# starlette
|
||||
# watchfiles
|
||||
asttokens==3.0.1
|
||||
asttokens==3.0.0
|
||||
# via stack-data
|
||||
async-timeout==5.0.1
|
||||
# via aiohttp
|
||||
attrs==25.4.0
|
||||
# via
|
||||
# aiohttp
|
||||
# dm-tree
|
||||
# jsonlines
|
||||
# jsonschema
|
||||
# referencing
|
||||
# rerun-sdk
|
||||
av==15.1.0
|
||||
# via lerobot
|
||||
bddl==1.0.1
|
||||
# via libero
|
||||
certifi==2025.10.5
|
||||
# via
|
||||
# lerobot
|
||||
# qwen-vl-utils
|
||||
certifi==2026.2.25
|
||||
# via
|
||||
# httpcore
|
||||
# httpx
|
||||
# requests
|
||||
# sentry-sdk
|
||||
cffi==2.0.0
|
||||
# via pymunk
|
||||
cfgv==3.5.0
|
||||
cfgv==3.4.0
|
||||
# via pre-commit
|
||||
charset-normalizer==3.4.5
|
||||
charset-normalizer==3.4.4
|
||||
# via requests
|
||||
click==8.3.1
|
||||
click==8.3.0
|
||||
# via
|
||||
# typer
|
||||
# uvicorn
|
||||
# wandb
|
||||
cloudpickle==3.1.2
|
||||
# via gymnasium
|
||||
cmake==4.1.3
|
||||
cloudpickle==3.1.1
|
||||
# via
|
||||
# gymnasium
|
||||
# libero
|
||||
cmake==4.1.0
|
||||
# via lerobot
|
||||
cmeel==0.59.0
|
||||
cmeel==0.57.3
|
||||
# via
|
||||
# cmeel-assimp
|
||||
# cmeel-boost
|
||||
@@ -105,17 +108,15 @@ cmeel-zlib==1.3.1
|
||||
# via cmeel-assimp
|
||||
coal-library==3.0.1
|
||||
# via pin
|
||||
contourpy==1.3.3
|
||||
# via
|
||||
# lerobot
|
||||
# matplotlib
|
||||
coverage[toml]==7.13.4
|
||||
contourpy==1.3.2
|
||||
# via matplotlib
|
||||
coverage[toml]==7.11.0
|
||||
# via pytest-cov
|
||||
cycler==0.12.1
|
||||
# via matplotlib
|
||||
datasets==4.6.1
|
||||
datasets==4.1.1
|
||||
# via lerobot
|
||||
debugpy==1.8.20
|
||||
debugpy==1.8.17
|
||||
# via lerobot
|
||||
decorator==5.2.1
|
||||
# via ipython
|
||||
@@ -129,7 +130,7 @@ dill==0.4.0
|
||||
# multiprocess
|
||||
distlib==0.4.0
|
||||
# via virtualenv
|
||||
dm-control==1.0.37
|
||||
dm-control==1.0.34
|
||||
# via gym-aloha
|
||||
dm-env==1.6
|
||||
# via dm-control
|
||||
@@ -137,55 +138,69 @@ dm-tree==0.1.9
|
||||
# via
|
||||
# dm-control
|
||||
# dm-env
|
||||
# lerobot
|
||||
docopt==0.6.2
|
||||
# via num2words
|
||||
draccus==0.10.0
|
||||
# via lerobot
|
||||
dynamixel-sdk==3.8.4
|
||||
# via lerobot
|
||||
easydict==1.13
|
||||
# via libero
|
||||
egl-probe @ git+https://github.com/huggingface/egl_probe.git
|
||||
# via
|
||||
# libero
|
||||
# robomimic
|
||||
eigenpy==3.10.3
|
||||
# via coal-library
|
||||
einops==0.8.2
|
||||
# via lerobot
|
||||
eiquadprog==1.2.9
|
||||
# via placo
|
||||
etils[epath,epy]==1.14.0
|
||||
# via mujoco
|
||||
executing==2.2.1
|
||||
# via stack-data
|
||||
faker==34.0.2
|
||||
# via lerobot
|
||||
farama-notifications==0.0.4
|
||||
# via gymnasium
|
||||
fastapi==0.135.1
|
||||
einops==0.8.1
|
||||
# via
|
||||
# lerobot
|
||||
# teleop
|
||||
# libero
|
||||
eiquadprog==1.2.9
|
||||
# via placo
|
||||
etils[epath,epy]==1.13.0
|
||||
# via mujoco
|
||||
exceptiongroup==1.3.0
|
||||
# via
|
||||
# anyio
|
||||
# ipython
|
||||
# pytest
|
||||
executing==2.2.1
|
||||
# via stack-data
|
||||
farama-notifications==0.0.4
|
||||
# via gymnasium
|
||||
fastapi==0.119.1
|
||||
# via teleop
|
||||
fastjsonschema==2.21.2
|
||||
# via nbformat
|
||||
feetech-servo-sdk==1.0.0
|
||||
# via lerobot
|
||||
filelock==3.25.0
|
||||
filelock==3.20.0
|
||||
# via
|
||||
# datasets
|
||||
# diffusers
|
||||
# huggingface-hub
|
||||
# python-discovery
|
||||
# torch
|
||||
# transformers
|
||||
# virtualenv
|
||||
fonttools==4.61.1
|
||||
fonttools==4.60.1
|
||||
# via matplotlib
|
||||
frozenlist==1.8.0
|
||||
# via
|
||||
# aiohttp
|
||||
# aiosignal
|
||||
fsspec[http]==2026.2.0
|
||||
fsspec[http]==2025.9.0
|
||||
# via
|
||||
# datasets
|
||||
# etils
|
||||
# huggingface-hub
|
||||
# torch
|
||||
future==1.0.0
|
||||
# via libero
|
||||
gitdb==4.0.12
|
||||
# via gitpython
|
||||
gitpython==3.1.46
|
||||
gitpython==3.1.45
|
||||
# via wandb
|
||||
glfw==2.10.0
|
||||
# via
|
||||
@@ -197,6 +212,7 @@ grpcio==1.73.1
|
||||
# lerobot
|
||||
# reachy2-sdk
|
||||
# reachy2-sdk-api
|
||||
# tensorboard
|
||||
grpcio-tools==1.73.1
|
||||
# via
|
||||
# lerobot
|
||||
@@ -207,67 +223,71 @@ gym-hil==0.1.13
|
||||
# via lerobot
|
||||
gym-pusht==0.1.6
|
||||
# via lerobot
|
||||
gymnasium==1.2.3
|
||||
gymnasium==1.2.1
|
||||
# via
|
||||
# gym-aloha
|
||||
# gym-hil
|
||||
# gym-pusht
|
||||
# lerobot
|
||||
# libero
|
||||
# metaworld
|
||||
h11==0.16.0
|
||||
# via
|
||||
# httpcore
|
||||
# uvicorn
|
||||
# via uvicorn
|
||||
h5py==3.15.1
|
||||
# via robomimic
|
||||
hebi-py==2.11.0
|
||||
# via lerobot
|
||||
hf-xet==1.3.2
|
||||
hf-transfer==0.1.9
|
||||
# via huggingface-hub
|
||||
hf-xet==1.1.10
|
||||
# via huggingface-hub
|
||||
hidapi==0.14.0.post4
|
||||
# via
|
||||
# gym-hil
|
||||
# lerobot
|
||||
httpcore==1.0.9
|
||||
# via httpx
|
||||
httptools==0.7.1
|
||||
# via uvicorn
|
||||
httpx==0.28.1
|
||||
# via
|
||||
# datasets
|
||||
# huggingface-hub
|
||||
huggingface-hub==1.6.0
|
||||
huggingface-hub[cli,hf-transfer]==0.35.3
|
||||
# via
|
||||
# accelerate
|
||||
# datasets
|
||||
# diffusers
|
||||
# lerobot
|
||||
# peft
|
||||
# timm
|
||||
# tokenizers
|
||||
# transformers
|
||||
identify==2.6.17
|
||||
hydra-core==1.3.2
|
||||
# via libero
|
||||
identify==2.6.15
|
||||
# via pre-commit
|
||||
idna==3.11
|
||||
# via
|
||||
# anyio
|
||||
# httpx
|
||||
# requests
|
||||
# yarl
|
||||
imageio[ffmpeg]==2.37.2
|
||||
imageio[ffmpeg]==2.37.0
|
||||
# via
|
||||
# gym-aloha
|
||||
# gym-hil
|
||||
# lerobot
|
||||
# metaworld
|
||||
# robomimic
|
||||
# scikit-image
|
||||
imageio-ffmpeg==0.6.0
|
||||
# via imageio
|
||||
importlib-metadata==8.7.1
|
||||
# via
|
||||
# imageio
|
||||
# robomimic
|
||||
importlib-metadata==8.7.0
|
||||
# via diffusers
|
||||
importlib-resources==6.5.2
|
||||
# via etils
|
||||
iniconfig==2.3.0
|
||||
# via pytest
|
||||
ipython==9.11.0
|
||||
inquirerpy==0.3.4
|
||||
# via huggingface-hub
|
||||
ipython==8.37.0
|
||||
# via meshcat
|
||||
ipython-pygments-lexers==1.1.1
|
||||
# via ipython
|
||||
ischedule==1.2.7
|
||||
# via placo
|
||||
jedi==0.19.2
|
||||
@@ -276,24 +296,44 @@ jinja2==3.1.6
|
||||
# via torch
|
||||
jsonlines==4.0.0
|
||||
# via lerobot
|
||||
jsonschema==4.25.1
|
||||
# via nbformat
|
||||
jsonschema-specifications==2025.9.1
|
||||
# via jsonschema
|
||||
jupyter-core==5.9.1
|
||||
# via nbformat
|
||||
jupytext==1.18.1
|
||||
# via bddl
|
||||
kiwisolver==1.4.9
|
||||
# via matplotlib
|
||||
labmaze==1.0.6
|
||||
# via dm-control
|
||||
lazy-loader==0.5
|
||||
lazy-loader==0.4
|
||||
# via scikit-image
|
||||
librt==0.8.1
|
||||
# via mypy
|
||||
libero @ git+https://github.com/huggingface/lerobot-libero.git@main
|
||||
# via lerobot
|
||||
llvmlite==0.45.1
|
||||
# via numba
|
||||
lxml==6.0.2
|
||||
# via dm-control
|
||||
markdown==3.9
|
||||
# via tensorboard
|
||||
markdown-it-py==4.0.0
|
||||
# via rich
|
||||
# via
|
||||
# jupytext
|
||||
# mdit-py-plugins
|
||||
markupsafe==3.0.3
|
||||
# via jinja2
|
||||
matplotlib==3.10.8
|
||||
# via lerobot
|
||||
# via
|
||||
# jinja2
|
||||
# werkzeug
|
||||
matplotlib==3.10.7
|
||||
# via
|
||||
# lerobot
|
||||
# libero
|
||||
matplotlib-inline==0.2.1
|
||||
# via ipython
|
||||
mdit-py-plugins==0.5.0
|
||||
# via jupytext
|
||||
mdurl==0.1.2
|
||||
# via markdown-it-py
|
||||
mergedeep==1.3.4
|
||||
@@ -306,35 +346,41 @@ mock-serial==0.0.1
|
||||
# via lerobot
|
||||
mpmath==1.3.0
|
||||
# via sympy
|
||||
mujoco==3.5.0
|
||||
mujoco==3.3.7
|
||||
# via
|
||||
# dm-control
|
||||
# gym-aloha
|
||||
# gym-hil
|
||||
# libero
|
||||
# metaworld
|
||||
multidict==6.7.1
|
||||
# robosuite
|
||||
multidict==6.7.0
|
||||
# via
|
||||
# aiohttp
|
||||
# yarl
|
||||
multiprocess==0.70.18
|
||||
multiprocess==0.70.16
|
||||
# via datasets
|
||||
mypy==1.19.1
|
||||
# via lerobot
|
||||
mypy-extensions==1.1.0
|
||||
# via typing-inspect
|
||||
nbformat==5.10.4
|
||||
# via jupytext
|
||||
networkx==3.4.2
|
||||
# via
|
||||
# mypy
|
||||
# typing-inspect
|
||||
networkx==3.6.1
|
||||
# via
|
||||
# bddl
|
||||
# scikit-image
|
||||
# torch
|
||||
nodeenv==1.10.0
|
||||
ninja==1.13.0
|
||||
# via lerobot
|
||||
nodeenv==1.9.1
|
||||
# via pre-commit
|
||||
num2words==0.5.14
|
||||
# via lerobot
|
||||
numba==0.62.1
|
||||
# via robosuite
|
||||
numpy==2.2.6
|
||||
# via
|
||||
# accelerate
|
||||
# bddl
|
||||
# cmeel-boost
|
||||
# contourpy
|
||||
# datasets
|
||||
@@ -343,14 +389,16 @@ numpy==2.2.6
|
||||
# dm-env
|
||||
# dm-tree
|
||||
# gymnasium
|
||||
# h5py
|
||||
# hebi-py
|
||||
# imageio
|
||||
# labmaze
|
||||
# lerobot
|
||||
# libero
|
||||
# matplotlib
|
||||
# meshcat
|
||||
# metaworld
|
||||
# mujoco
|
||||
# numba
|
||||
# opencv-python
|
||||
# opencv-python-headless
|
||||
# pandas
|
||||
@@ -358,18 +406,26 @@ numpy==2.2.6
|
||||
# pyquaternion
|
||||
# reachy2-sdk
|
||||
# rerun-sdk
|
||||
# robomimic
|
||||
# robosuite
|
||||
# scikit-image
|
||||
# scipy
|
||||
# shapely
|
||||
# teleop
|
||||
# tensorboard
|
||||
# tensorboardx
|
||||
# tifffile
|
||||
# torchvision
|
||||
# transformers
|
||||
# transforms3d
|
||||
opencv-python==4.13.0.92
|
||||
omegaconf==2.3.0
|
||||
# via hydra-core
|
||||
opencv-python==4.12.0.88
|
||||
# via
|
||||
# gym-pusht
|
||||
# libero
|
||||
# reachy2-sdk
|
||||
# robosuite
|
||||
opencv-python-headless==4.12.0.88
|
||||
# via lerobot
|
||||
orderly-set==5.5.0
|
||||
@@ -379,87 +435,97 @@ packaging==25.0
|
||||
# accelerate
|
||||
# datasets
|
||||
# huggingface-hub
|
||||
# hydra-core
|
||||
# jupytext
|
||||
# lazy-loader
|
||||
# lerobot
|
||||
# matplotlib
|
||||
# peft
|
||||
# pytest
|
||||
# qwen-vl-utils
|
||||
# reachy2-sdk
|
||||
# scikit-image
|
||||
# tensorboard
|
||||
# tensorboardx
|
||||
# transformers
|
||||
# wandb
|
||||
pandas==2.3.3
|
||||
# via
|
||||
# datasets
|
||||
# lerobot
|
||||
parso==0.8.6
|
||||
parso==0.8.5
|
||||
# via jedi
|
||||
pathspec==1.0.4
|
||||
# via mypy
|
||||
peft==0.18.1
|
||||
peft==0.17.1
|
||||
# via lerobot
|
||||
pexpect==4.9.0
|
||||
# via ipython
|
||||
pillow==12.1.1
|
||||
pfzy==0.3.4
|
||||
# via inquirerpy
|
||||
pillow==12.0.0
|
||||
# via
|
||||
# diffusers
|
||||
# imageio
|
||||
# lerobot
|
||||
# matplotlib
|
||||
# meshcat
|
||||
# qwen-vl-utils
|
||||
# rerun-sdk
|
||||
# robosuite
|
||||
# scikit-image
|
||||
# tensorboard
|
||||
# torchvision
|
||||
pin==3.4.0
|
||||
# via placo
|
||||
placo==0.9.16
|
||||
placo==0.9.14
|
||||
# via lerobot
|
||||
platformdirs==4.9.4
|
||||
platformdirs==4.5.0
|
||||
# via
|
||||
# python-discovery
|
||||
# jupyter-core
|
||||
# virtualenv
|
||||
# wandb
|
||||
pluggy==1.6.0
|
||||
# via
|
||||
# pytest
|
||||
# pytest-cov
|
||||
pre-commit==4.5.1
|
||||
pre-commit==4.3.0
|
||||
# via lerobot
|
||||
prompt-toolkit==3.0.52
|
||||
# via ipython
|
||||
# via
|
||||
# inquirerpy
|
||||
# ipython
|
||||
propcache==0.4.1
|
||||
# via
|
||||
# aiohttp
|
||||
# yarl
|
||||
protobuf==6.31.1
|
||||
protobuf==6.31.0
|
||||
# via
|
||||
# dm-control
|
||||
# grpcio-tools
|
||||
# lerobot
|
||||
# reachy2-sdk
|
||||
# reachy2-sdk-api
|
||||
# tensorboard
|
||||
# tensorboardx
|
||||
# wandb
|
||||
psutil==7.2.2
|
||||
psutil==7.1.1
|
||||
# via
|
||||
# accelerate
|
||||
# imageio
|
||||
# peft
|
||||
# robomimic
|
||||
ptyprocess==0.7.0
|
||||
# via pexpect
|
||||
pure-eval==0.2.3
|
||||
# via stack-data
|
||||
pyarrow==23.0.1
|
||||
pyarrow==21.0.0
|
||||
# via
|
||||
# datasets
|
||||
# rerun-sdk
|
||||
pycparser==3.0
|
||||
pycparser==2.23
|
||||
# via cffi
|
||||
pydantic==2.12.5
|
||||
pydantic==2.12.3
|
||||
# via
|
||||
# fastapi
|
||||
# wandb
|
||||
pydantic-core==2.41.5
|
||||
pydantic-core==2.41.4
|
||||
# via pydantic
|
||||
pygame==2.6.1
|
||||
# via
|
||||
@@ -469,35 +535,33 @@ pygame==2.6.1
|
||||
pygments==2.19.2
|
||||
# via
|
||||
# ipython
|
||||
# ipython-pygments-lexers
|
||||
# pytest
|
||||
# rich
|
||||
pymunk==6.11.1
|
||||
# via
|
||||
# gym-pusht
|
||||
# lerobot
|
||||
pyngrok==7.5.1
|
||||
pyngrok==7.4.1
|
||||
# via meshcat
|
||||
pynput==1.8.1
|
||||
# via
|
||||
# gym-hil
|
||||
# lerobot
|
||||
pyobjc-core==12.1
|
||||
pyobjc-core==12.0
|
||||
# via
|
||||
# pyobjc-framework-applicationservices
|
||||
# pyobjc-framework-cocoa
|
||||
# pyobjc-framework-coretext
|
||||
# pyobjc-framework-quartz
|
||||
pyobjc-framework-applicationservices==12.1
|
||||
pyobjc-framework-applicationservices==12.0
|
||||
# via pynput
|
||||
pyobjc-framework-cocoa==12.1
|
||||
pyobjc-framework-cocoa==12.0
|
||||
# via
|
||||
# pyobjc-framework-applicationservices
|
||||
# pyobjc-framework-coretext
|
||||
# pyobjc-framework-quartz
|
||||
pyobjc-framework-coretext==12.1
|
||||
pyobjc-framework-coretext==12.0
|
||||
# via pyobjc-framework-applicationservices
|
||||
pyobjc-framework-quartz==12.1
|
||||
pyobjc-framework-quartz==12.0
|
||||
# via
|
||||
# pynput
|
||||
# pyobjc-framework-applicationservices
|
||||
@@ -506,13 +570,13 @@ pyopengl==3.1.10
|
||||
# via
|
||||
# dm-control
|
||||
# mujoco
|
||||
pyparsing==3.3.2
|
||||
pyparsing==3.2.5
|
||||
# via
|
||||
# dm-control
|
||||
# matplotlib
|
||||
pyquaternion==0.9.9
|
||||
# via reachy2-sdk
|
||||
pyrealsense2-macosx==2.56.5
|
||||
pyrealsense2-macosx==2.54.2
|
||||
# via lerobot
|
||||
pyserial==3.5
|
||||
# via
|
||||
@@ -521,6 +585,7 @@ pyserial==3.5
|
||||
# lerobot
|
||||
pytest==8.4.2
|
||||
# via
|
||||
# bddl
|
||||
# lerobot
|
||||
# pytest-cov
|
||||
# pytest-timeout
|
||||
@@ -531,14 +596,11 @@ pytest-timeout==2.4.0
|
||||
# via lerobot
|
||||
python-dateutil==2.9.0.post0
|
||||
# via
|
||||
# faker
|
||||
# matplotlib
|
||||
# pandas
|
||||
python-discovery==1.1.1
|
||||
# via virtualenv
|
||||
python-dotenv==1.2.2
|
||||
python-dotenv==1.1.1
|
||||
# via uvicorn
|
||||
pytz==2026.1.post1
|
||||
pytz==2025.2
|
||||
# via pandas
|
||||
pyyaml==6.0.3
|
||||
# via
|
||||
@@ -547,10 +609,13 @@ pyyaml==6.0.3
|
||||
# draccus
|
||||
# hebi-py
|
||||
# huggingface-hub
|
||||
# jupytext
|
||||
# omegaconf
|
||||
# peft
|
||||
# pre-commit
|
||||
# pyngrok
|
||||
# pyyaml-include
|
||||
# timm
|
||||
# transformers
|
||||
# uvicorn
|
||||
# wandb
|
||||
@@ -560,13 +625,15 @@ pyzmq==27.1.0
|
||||
# via
|
||||
# lerobot
|
||||
# meshcat
|
||||
qwen-vl-utils==0.0.14
|
||||
# via lerobot
|
||||
reachy2-sdk==1.0.15
|
||||
reachy2-sdk==1.0.14
|
||||
# via lerobot
|
||||
reachy2-sdk-api==1.0.21
|
||||
# via reachy2-sdk
|
||||
regex==2026.2.28
|
||||
referencing==0.37.0
|
||||
# via
|
||||
# jsonschema
|
||||
# jsonschema-specifications
|
||||
regex==2025.10.23
|
||||
# via
|
||||
# diffusers
|
||||
# transformers
|
||||
@@ -575,150 +642,184 @@ requests==2.32.5
|
||||
# datasets
|
||||
# diffusers
|
||||
# dm-control
|
||||
# qwen-vl-utils
|
||||
# huggingface-hub
|
||||
# teleop
|
||||
# transformers
|
||||
# wandb
|
||||
rerun-sdk==0.26.2
|
||||
rerun-sdk==0.26.1
|
||||
# via lerobot
|
||||
rhoban-cmeel-jsoncpp==1.9.4.9
|
||||
# via placo
|
||||
rich==14.3.3
|
||||
# via typer
|
||||
safetensors==0.7.0
|
||||
robomimic==0.2.0
|
||||
# via libero
|
||||
robosuite==1.4.0
|
||||
# via libero
|
||||
rpds-py==0.28.0
|
||||
# via
|
||||
# jsonschema
|
||||
# referencing
|
||||
safetensors==0.6.2
|
||||
# via
|
||||
# accelerate
|
||||
# diffusers
|
||||
# lerobot
|
||||
# peft
|
||||
# timm
|
||||
# transformers
|
||||
scikit-image==0.25.2
|
||||
# via
|
||||
# gym-pusht
|
||||
# lerobot
|
||||
scipy==1.17.1
|
||||
scipy==1.15.3
|
||||
# via
|
||||
# dm-control
|
||||
# lerobot
|
||||
# metaworld
|
||||
# robosuite
|
||||
# scikit-image
|
||||
# torchdiffeq
|
||||
sentry-sdk==2.54.0
|
||||
sentry-sdk==2.42.1
|
||||
# via wandb
|
||||
shapely==2.1.2
|
||||
# via gym-pusht
|
||||
shellingham==1.5.4
|
||||
# via typer
|
||||
six==1.17.0
|
||||
# via
|
||||
# pynput
|
||||
# python-dateutil
|
||||
smmap==5.0.3
|
||||
smmap==5.0.2
|
||||
# via gitdb
|
||||
sniffio==1.3.1
|
||||
# via anyio
|
||||
stack-data==0.6.3
|
||||
# via ipython
|
||||
starlette==0.52.1
|
||||
starlette==0.48.0
|
||||
# via fastapi
|
||||
sympy==1.14.0
|
||||
# via torch
|
||||
teleop==0.1.4
|
||||
teleop==0.1.2
|
||||
# via lerobot
|
||||
termcolor==3.3.0
|
||||
# via lerobot
|
||||
tifffile==2026.3.3
|
||||
tensorboard==2.20.0
|
||||
# via robomimic
|
||||
tensorboard-data-server==0.7.2
|
||||
# via tensorboard
|
||||
tensorboardx==2.6.4
|
||||
# via robomimic
|
||||
termcolor==3.1.0
|
||||
# via
|
||||
# lerobot
|
||||
# robomimic
|
||||
thop==0.1.1.post2209072238
|
||||
# via libero
|
||||
tifffile==2025.5.10
|
||||
# via scikit-image
|
||||
tokenizers==0.22.2
|
||||
timm==1.0.20
|
||||
# via lerobot
|
||||
tokenizers==0.22.1
|
||||
# via transformers
|
||||
toml==0.10.2
|
||||
# via draccus
|
||||
torch==2.10.0
|
||||
tomli==2.3.0
|
||||
# via
|
||||
# cmeel
|
||||
# coverage
|
||||
# jupytext
|
||||
# pytest
|
||||
torch==2.7.1
|
||||
# via
|
||||
# accelerate
|
||||
# lerobot
|
||||
# peft
|
||||
# torchdiffeq
|
||||
# robomimic
|
||||
# thop
|
||||
# timm
|
||||
# torchvision
|
||||
torchcodec==0.10.0
|
||||
torchcodec==0.5
|
||||
# via lerobot
|
||||
torchdiffeq==0.2.5
|
||||
# via lerobot
|
||||
torchvision==0.25.0
|
||||
# via lerobot
|
||||
tornado==6.5.4
|
||||
torchvision==0.22.1
|
||||
# via
|
||||
# lerobot
|
||||
# robomimic
|
||||
# timm
|
||||
tornado==6.5.2
|
||||
# via meshcat
|
||||
tqdm==4.67.3
|
||||
tqdm==4.67.1
|
||||
# via
|
||||
# datasets
|
||||
# dm-control
|
||||
# huggingface-hub
|
||||
# peft
|
||||
# robomimic
|
||||
# transformers
|
||||
traitlets==5.14.3
|
||||
# via
|
||||
# ipython
|
||||
# jupyter-core
|
||||
# matplotlib-inline
|
||||
transformers==5.3.0
|
||||
# nbformat
|
||||
transformers==4.57.1
|
||||
# via
|
||||
# lerobot
|
||||
# libero
|
||||
# peft
|
||||
transforms3d==0.4.2
|
||||
# via teleop
|
||||
typer==0.24.1
|
||||
# via
|
||||
# huggingface-hub
|
||||
# transformers
|
||||
typing-extensions==4.15.0
|
||||
# via
|
||||
# aiosignal
|
||||
# anyio
|
||||
# etils
|
||||
# faker
|
||||
# exceptiongroup
|
||||
# fastapi
|
||||
# gymnasium
|
||||
# huggingface-hub
|
||||
# mypy
|
||||
# ipython
|
||||
# multidict
|
||||
# pydantic
|
||||
# pydantic-core
|
||||
# referencing
|
||||
# rerun-sdk
|
||||
# starlette
|
||||
# torch
|
||||
# typing-inspect
|
||||
# typing-inspection
|
||||
# uvicorn
|
||||
# virtualenv
|
||||
# wandb
|
||||
typing-inspect==0.9.0
|
||||
# via draccus
|
||||
typing-inspection==0.4.2
|
||||
# via
|
||||
# fastapi
|
||||
# pydantic
|
||||
tzdata==2025.3
|
||||
# via pydantic
|
||||
tzdata==2025.2
|
||||
# via pandas
|
||||
u-msgpack-python==2.8.0
|
||||
# via meshcat
|
||||
urllib3==2.6.3
|
||||
urllib3==2.5.0
|
||||
# via
|
||||
# requests
|
||||
# sentry-sdk
|
||||
uvicorn[standard]==0.41.0
|
||||
uvicorn[standard]==0.38.0
|
||||
# via teleop
|
||||
uvloop==0.22.1
|
||||
# via uvicorn
|
||||
virtualenv==21.1.0
|
||||
virtualenv==20.35.3
|
||||
# via pre-commit
|
||||
wandb==0.24.2
|
||||
# via lerobot
|
||||
wandb==0.21.4
|
||||
# via
|
||||
# lerobot
|
||||
# libero
|
||||
watchfiles==1.1.1
|
||||
# via uvicorn
|
||||
wcwidth==0.6.0
|
||||
wcwidth==0.2.14
|
||||
# via prompt-toolkit
|
||||
websocket-client==1.9.0
|
||||
# via teleop
|
||||
websockets==16.0
|
||||
websockets==15.0.1
|
||||
# via uvicorn
|
||||
wrapt==2.1.2
|
||||
werkzeug==3.1.3
|
||||
# via tensorboard
|
||||
wrapt==2.0.0
|
||||
# via dm-tree
|
||||
xxhash==3.6.0
|
||||
# via datasets
|
||||
yarl==1.23.0
|
||||
yarl==1.22.0
|
||||
# via aiohttp
|
||||
zipp==3.23.0
|
||||
# via
|
||||
|
||||
@@ -1,12 +1,12 @@
|
||||
#
|
||||
# This file is autogenerated by pip-compile with Python 3.12
|
||||
# This file is autogenerated by pip-compile with Python 3.10
|
||||
# by the following command:
|
||||
#
|
||||
# pip-compile --output-file=requirements-ubuntu.txt requirements.in
|
||||
#
|
||||
-e .[all]
|
||||
# via -[all]
|
||||
absl-py==2.4.0
|
||||
absl-py==2.3.1
|
||||
# via
|
||||
# dm-control
|
||||
# dm-env
|
||||
@@ -14,33 +14,30 @@ absl-py==2.4.0
|
||||
# labmaze
|
||||
# mujoco
|
||||
# tensorboard
|
||||
accelerate==1.13.0
|
||||
accelerate==1.11.0
|
||||
# via
|
||||
# lerobot
|
||||
# peft
|
||||
aiohappyeyeballs==2.6.1
|
||||
# via aiohttp
|
||||
aiohttp==3.13.3
|
||||
aiohttp==3.13.1
|
||||
# via fsspec
|
||||
aiosignal==1.4.0
|
||||
# via aiohttp
|
||||
annotated-doc==0.0.4
|
||||
# via
|
||||
# fastapi
|
||||
# typer
|
||||
annotated-types==0.7.0
|
||||
# via pydantic
|
||||
antlr4-python3-runtime==4.9.3
|
||||
# via
|
||||
# hydra-core
|
||||
# omegaconf
|
||||
anyio==4.12.1
|
||||
anyio==4.11.0
|
||||
# via
|
||||
# httpx
|
||||
# starlette
|
||||
# watchfiles
|
||||
asttokens==3.0.1
|
||||
asttokens==3.0.0
|
||||
# via stack-data
|
||||
async-timeout==5.0.1
|
||||
# via aiohttp
|
||||
attrs==25.4.0
|
||||
# via
|
||||
# aiohttp
|
||||
@@ -50,35 +47,30 @@ attrs==25.4.0
|
||||
# referencing
|
||||
# rerun-sdk
|
||||
av==15.1.0
|
||||
# via
|
||||
# lerobot
|
||||
# qwen-vl-utils
|
||||
# via lerobot
|
||||
bddl==1.0.1
|
||||
# via hf-libero
|
||||
certifi==2026.2.25
|
||||
# via libero
|
||||
certifi==2025.10.5
|
||||
# via
|
||||
# httpcore
|
||||
# httpx
|
||||
# requests
|
||||
# sentry-sdk
|
||||
cffi==2.0.0
|
||||
# via pymunk
|
||||
cfgv==3.5.0
|
||||
cfgv==3.4.0
|
||||
# via pre-commit
|
||||
charset-normalizer==3.4.5
|
||||
charset-normalizer==3.4.4
|
||||
# via requests
|
||||
click==8.3.1
|
||||
click==8.3.0
|
||||
# via
|
||||
# typer
|
||||
# uvicorn
|
||||
# wandb
|
||||
cloudpickle==3.1.2
|
||||
cloudpickle==3.1.1
|
||||
# via
|
||||
# gymnasium
|
||||
# hf-libero
|
||||
cmake==4.1.3
|
||||
# libero
|
||||
cmake==4.1.0
|
||||
# via lerobot
|
||||
cmeel==0.59.0
|
||||
cmeel==0.57.3
|
||||
# via
|
||||
# cmeel-assimp
|
||||
# cmeel-boost
|
||||
@@ -116,24 +108,20 @@ cmeel-zlib==1.3.1
|
||||
# via cmeel-assimp
|
||||
coal-library==3.0.1
|
||||
# via pin
|
||||
contourpy==1.3.3
|
||||
# via
|
||||
# lerobot
|
||||
# matplotlib
|
||||
coverage[toml]==7.13.4
|
||||
contourpy==1.3.2
|
||||
# via matplotlib
|
||||
coverage[toml]==7.11.0
|
||||
# via pytest-cov
|
||||
cuda-bindings==12.9.4
|
||||
# via torch
|
||||
cuda-pathfinder==1.4.1
|
||||
# via cuda-bindings
|
||||
cycler==0.12.1
|
||||
# via matplotlib
|
||||
datasets==4.6.1
|
||||
datasets==4.1.1
|
||||
# via lerobot
|
||||
debugpy==1.8.20
|
||||
debugpy==1.8.17
|
||||
# via lerobot
|
||||
decorator==5.2.1
|
||||
# via ipython
|
||||
decord==0.6.0
|
||||
# via lerobot
|
||||
deepdiff==8.6.1
|
||||
# via lerobot
|
||||
diffusers==0.35.2
|
||||
@@ -144,7 +132,7 @@ dill==0.4.0
|
||||
# multiprocess
|
||||
distlib==0.4.0
|
||||
# via virtualenv
|
||||
dm-control==1.0.37
|
||||
dm-control==1.0.34
|
||||
# via gym-aloha
|
||||
dm-env==1.6
|
||||
# via dm-control
|
||||
@@ -152,6 +140,7 @@ dm-tree==0.1.9
|
||||
# via
|
||||
# dm-control
|
||||
# dm-env
|
||||
# lerobot
|
||||
docopt==0.6.2
|
||||
# via num2words
|
||||
draccus==0.10.0
|
||||
@@ -159,60 +148,66 @@ draccus==0.10.0
|
||||
dynamixel-sdk==3.8.4
|
||||
# via lerobot
|
||||
easydict==1.13
|
||||
# via hf-libero
|
||||
egl-probe==1.0.2
|
||||
# via robomimic
|
||||
# via libero
|
||||
egl-probe @ git+https://github.com/huggingface/egl_probe.git
|
||||
# via
|
||||
# libero
|
||||
# robomimic
|
||||
eigenpy==3.10.3
|
||||
# via coal-library
|
||||
einops==0.8.2
|
||||
einops==0.8.1
|
||||
# via
|
||||
# hf-libero
|
||||
# flash-attn
|
||||
# lerobot
|
||||
# libero
|
||||
eiquadprog==1.2.9
|
||||
# via placo
|
||||
etils[epath,epy]==1.14.0
|
||||
etils[epath,epy]==1.13.0
|
||||
# via mujoco
|
||||
evdev==1.9.3
|
||||
evdev==1.9.2
|
||||
# via pynput
|
||||
exceptiongroup==1.3.0
|
||||
# via
|
||||
# anyio
|
||||
# ipython
|
||||
# pytest
|
||||
executing==2.2.1
|
||||
# via stack-data
|
||||
faker==34.0.2
|
||||
# via lerobot
|
||||
farama-notifications==0.0.4
|
||||
# via gymnasium
|
||||
fastapi==0.135.1
|
||||
# via
|
||||
# lerobot
|
||||
# teleop
|
||||
fastapi==0.119.1
|
||||
# via teleop
|
||||
fastjsonschema==2.21.2
|
||||
# via nbformat
|
||||
feetech-servo-sdk==1.0.0
|
||||
# via lerobot
|
||||
filelock==3.25.0
|
||||
filelock==3.20.0
|
||||
# via
|
||||
# datasets
|
||||
# diffusers
|
||||
# huggingface-hub
|
||||
# python-discovery
|
||||
# torch
|
||||
# transformers
|
||||
# virtualenv
|
||||
fonttools==4.61.1
|
||||
flash-attn==2.8.3
|
||||
# via lerobot
|
||||
fonttools==4.60.1
|
||||
# via matplotlib
|
||||
frozenlist==1.8.0
|
||||
# via
|
||||
# aiohttp
|
||||
# aiosignal
|
||||
fsspec[http]==2026.2.0
|
||||
fsspec[http]==2025.9.0
|
||||
# via
|
||||
# datasets
|
||||
# etils
|
||||
# huggingface-hub
|
||||
# torch
|
||||
future==1.0.0
|
||||
# via hf-libero
|
||||
# via libero
|
||||
gitdb==4.0.12
|
||||
# via gitpython
|
||||
gitpython==3.1.46
|
||||
gitpython==3.1.45
|
||||
# via wandb
|
||||
glfw==2.10.0
|
||||
# via
|
||||
@@ -235,60 +230,50 @@ gym-hil==0.1.13
|
||||
# via lerobot
|
||||
gym-pusht==0.1.6
|
||||
# via lerobot
|
||||
gymnasium==1.2.3
|
||||
gymnasium==1.2.1
|
||||
# via
|
||||
# gym-aloha
|
||||
# gym-hil
|
||||
# gym-pusht
|
||||
# hf-libero
|
||||
# lerobot
|
||||
# libero
|
||||
# metaworld
|
||||
h11==0.16.0
|
||||
# via
|
||||
# httpcore
|
||||
# uvicorn
|
||||
h5py==3.16.0
|
||||
# via uvicorn
|
||||
h5py==3.15.1
|
||||
# via robomimic
|
||||
hebi-py==2.11.0
|
||||
# via lerobot
|
||||
hf-egl-probe==1.0.2
|
||||
# via hf-libero
|
||||
hf-libero==0.1.3
|
||||
# via lerobot
|
||||
hf-xet==1.3.2
|
||||
hf-transfer==0.1.9
|
||||
# via huggingface-hub
|
||||
hf-xet==1.1.10
|
||||
# via huggingface-hub
|
||||
hidapi==0.14.0.post4
|
||||
# via
|
||||
# gym-hil
|
||||
# lerobot
|
||||
httpcore==1.0.9
|
||||
# via httpx
|
||||
httptools==0.7.1
|
||||
# via uvicorn
|
||||
httpx==0.28.1
|
||||
# via
|
||||
# datasets
|
||||
# huggingface-hub
|
||||
huggingface-hub==1.6.0
|
||||
huggingface-hub[cli,hf-transfer]==0.35.3
|
||||
# via
|
||||
# accelerate
|
||||
# datasets
|
||||
# diffusers
|
||||
# lerobot
|
||||
# peft
|
||||
# timm
|
||||
# tokenizers
|
||||
# transformers
|
||||
hydra-core==1.3.2
|
||||
# via hf-libero
|
||||
identify==2.6.17
|
||||
# via libero
|
||||
identify==2.6.15
|
||||
# via pre-commit
|
||||
idna==3.11
|
||||
# via
|
||||
# anyio
|
||||
# httpx
|
||||
# requests
|
||||
# yarl
|
||||
imageio[ffmpeg]==2.37.2
|
||||
imageio[ffmpeg]==2.37.0
|
||||
# via
|
||||
# gym-aloha
|
||||
# gym-hil
|
||||
@@ -300,14 +285,16 @@ imageio-ffmpeg==0.6.0
|
||||
# via
|
||||
# imageio
|
||||
# robomimic
|
||||
importlib-metadata==8.7.1
|
||||
importlib-metadata==8.7.0
|
||||
# via diffusers
|
||||
importlib-resources==6.5.2
|
||||
# via etils
|
||||
iniconfig==2.3.0
|
||||
# via pytest
|
||||
ipython==9.11.0
|
||||
inquirerpy==0.3.4
|
||||
# via huggingface-hub
|
||||
ipython==8.37.0
|
||||
# via meshcat
|
||||
ipython-pygments-lexers==1.1.1
|
||||
# via ipython
|
||||
ischedule==1.2.7
|
||||
# via placo
|
||||
jedi==0.19.2
|
||||
@@ -316,41 +303,40 @@ jinja2==3.1.6
|
||||
# via torch
|
||||
jsonlines==4.0.0
|
||||
# via lerobot
|
||||
jsonschema==4.26.0
|
||||
jsonschema==4.25.1
|
||||
# via nbformat
|
||||
jsonschema-specifications==2025.9.1
|
||||
# via jsonschema
|
||||
jupyter-core==5.9.1
|
||||
# via nbformat
|
||||
jupytext==1.19.1
|
||||
jupytext==1.18.1
|
||||
# via bddl
|
||||
kiwisolver==1.4.9
|
||||
# via matplotlib
|
||||
labmaze==1.0.6
|
||||
# via dm-control
|
||||
lazy-loader==0.5
|
||||
lazy-loader==0.4
|
||||
# via scikit-image
|
||||
librt==0.8.1
|
||||
# via mypy
|
||||
llvmlite==0.46.0
|
||||
libero @ git+https://github.com/huggingface/lerobot-libero.git@main
|
||||
# via lerobot
|
||||
llvmlite==0.45.1
|
||||
# via numba
|
||||
lxml==6.0.2
|
||||
# via dm-control
|
||||
markdown==3.10.2
|
||||
markdown==3.9
|
||||
# via tensorboard
|
||||
markdown-it-py==4.0.0
|
||||
# via
|
||||
# jupytext
|
||||
# mdit-py-plugins
|
||||
# rich
|
||||
markupsafe==3.0.3
|
||||
# via
|
||||
# jinja2
|
||||
# werkzeug
|
||||
matplotlib==3.10.8
|
||||
matplotlib==3.10.7
|
||||
# via
|
||||
# hf-libero
|
||||
# lerobot
|
||||
# libero
|
||||
matplotlib-inline==0.2.1
|
||||
# via ipython
|
||||
mdit-py-plugins==0.5.0
|
||||
@@ -367,38 +353,36 @@ mock-serial==0.0.1
|
||||
# via lerobot
|
||||
mpmath==1.3.0
|
||||
# via sympy
|
||||
mujoco==3.5.0
|
||||
mujoco==3.3.7
|
||||
# via
|
||||
# dm-control
|
||||
# gym-aloha
|
||||
# gym-hil
|
||||
# hf-libero
|
||||
# libero
|
||||
# metaworld
|
||||
# robosuite
|
||||
multidict==6.7.1
|
||||
multidict==6.7.0
|
||||
# via
|
||||
# aiohttp
|
||||
# yarl
|
||||
multiprocess==0.70.18
|
||||
multiprocess==0.70.16
|
||||
# via datasets
|
||||
mypy==1.19.1
|
||||
# via lerobot
|
||||
mypy-extensions==1.1.0
|
||||
# via
|
||||
# mypy
|
||||
# typing-inspect
|
||||
# via typing-inspect
|
||||
nbformat==5.10.4
|
||||
# via jupytext
|
||||
networkx==3.6.1
|
||||
networkx==3.4.2
|
||||
# via
|
||||
# bddl
|
||||
# scikit-image
|
||||
# torch
|
||||
nodeenv==1.10.0
|
||||
ninja==1.13.0
|
||||
# via lerobot
|
||||
nodeenv==1.9.1
|
||||
# via pre-commit
|
||||
num2words==0.5.14
|
||||
# via lerobot
|
||||
numba==0.64.0
|
||||
numba==0.62.1
|
||||
# via robosuite
|
||||
numpy==2.2.6
|
||||
# via
|
||||
@@ -407,6 +391,7 @@ numpy==2.2.6
|
||||
# cmeel-boost
|
||||
# contourpy
|
||||
# datasets
|
||||
# decord
|
||||
# diffusers
|
||||
# dm-control
|
||||
# dm-env
|
||||
@@ -414,10 +399,9 @@ numpy==2.2.6
|
||||
# gymnasium
|
||||
# h5py
|
||||
# hebi-py
|
||||
# hf-libero
|
||||
# imageio
|
||||
# labmaze
|
||||
# lerobot
|
||||
# libero
|
||||
# matplotlib
|
||||
# meshcat
|
||||
# metaworld
|
||||
@@ -442,51 +426,49 @@ numpy==2.2.6
|
||||
# torchvision
|
||||
# transformers
|
||||
# transforms3d
|
||||
nvidia-cublas-cu12==12.8.4.1
|
||||
nvidia-cublas-cu12==12.6.4.1
|
||||
# via
|
||||
# nvidia-cudnn-cu12
|
||||
# nvidia-cusolver-cu12
|
||||
# torch
|
||||
nvidia-cuda-cupti-cu12==12.8.90
|
||||
nvidia-cuda-cupti-cu12==12.6.80
|
||||
# via torch
|
||||
nvidia-cuda-nvrtc-cu12==12.8.93
|
||||
nvidia-cuda-nvrtc-cu12==12.6.77
|
||||
# via torch
|
||||
nvidia-cuda-runtime-cu12==12.8.90
|
||||
nvidia-cuda-runtime-cu12==12.6.77
|
||||
# via torch
|
||||
nvidia-cudnn-cu12==9.10.2.21
|
||||
nvidia-cudnn-cu12==9.5.1.17
|
||||
# via torch
|
||||
nvidia-cufft-cu12==11.3.3.83
|
||||
nvidia-cufft-cu12==11.3.0.4
|
||||
# via torch
|
||||
nvidia-cufile-cu12==1.13.1.3
|
||||
nvidia-cufile-cu12==1.11.1.6
|
||||
# via torch
|
||||
nvidia-curand-cu12==10.3.9.90
|
||||
nvidia-curand-cu12==10.3.7.77
|
||||
# via torch
|
||||
nvidia-cusolver-cu12==11.7.3.90
|
||||
nvidia-cusolver-cu12==11.7.1.2
|
||||
# via torch
|
||||
nvidia-cusparse-cu12==12.5.8.93
|
||||
nvidia-cusparse-cu12==12.5.4.2
|
||||
# via
|
||||
# nvidia-cusolver-cu12
|
||||
# torch
|
||||
nvidia-cusparselt-cu12==0.7.1
|
||||
nvidia-cusparselt-cu12==0.6.3
|
||||
# via torch
|
||||
nvidia-nccl-cu12==2.27.5
|
||||
nvidia-nccl-cu12==2.26.2
|
||||
# via torch
|
||||
nvidia-nvjitlink-cu12==12.8.93
|
||||
nvidia-nvjitlink-cu12==12.6.85
|
||||
# via
|
||||
# nvidia-cufft-cu12
|
||||
# nvidia-cusolver-cu12
|
||||
# nvidia-cusparse-cu12
|
||||
# torch
|
||||
nvidia-nvshmem-cu12==3.4.5
|
||||
# via torch
|
||||
nvidia-nvtx-cu12==12.8.90
|
||||
nvidia-nvtx-cu12==12.6.77
|
||||
# via torch
|
||||
omegaconf==2.3.0
|
||||
# via hydra-core
|
||||
opencv-python==4.13.0.92
|
||||
opencv-python==4.12.0.88
|
||||
# via
|
||||
# gym-pusht
|
||||
# hf-libero
|
||||
# libero
|
||||
# reachy2-sdk
|
||||
# robosuite
|
||||
opencv-python-headless==4.12.0.88
|
||||
@@ -505,7 +487,6 @@ packaging==25.0
|
||||
# matplotlib
|
||||
# peft
|
||||
# pytest
|
||||
# qwen-vl-utils
|
||||
# reachy2-sdk
|
||||
# scikit-image
|
||||
# tensorboard
|
||||
@@ -516,21 +497,21 @@ pandas==2.3.3
|
||||
# via
|
||||
# datasets
|
||||
# lerobot
|
||||
parso==0.8.6
|
||||
parso==0.8.5
|
||||
# via jedi
|
||||
pathspec==1.0.4
|
||||
# via mypy
|
||||
peft==0.18.1
|
||||
peft==0.17.1
|
||||
# via lerobot
|
||||
pexpect==4.9.0
|
||||
# via ipython
|
||||
pillow==12.1.1
|
||||
pfzy==0.3.4
|
||||
# via inquirerpy
|
||||
pillow==12.0.0
|
||||
# via
|
||||
# diffusers
|
||||
# imageio
|
||||
# lerobot
|
||||
# matplotlib
|
||||
# meshcat
|
||||
# qwen-vl-utils
|
||||
# rerun-sdk
|
||||
# robosuite
|
||||
# scikit-image
|
||||
@@ -538,27 +519,28 @@ pillow==12.1.1
|
||||
# torchvision
|
||||
pin==3.4.0
|
||||
# via placo
|
||||
placo==0.9.16
|
||||
placo==0.9.14
|
||||
# via lerobot
|
||||
platformdirs==4.9.4
|
||||
platformdirs==4.5.0
|
||||
# via
|
||||
# jupyter-core
|
||||
# python-discovery
|
||||
# virtualenv
|
||||
# wandb
|
||||
pluggy==1.6.0
|
||||
# via
|
||||
# pytest
|
||||
# pytest-cov
|
||||
pre-commit==4.5.1
|
||||
pre-commit==4.3.0
|
||||
# via lerobot
|
||||
prompt-toolkit==3.0.52
|
||||
# via ipython
|
||||
# via
|
||||
# inquirerpy
|
||||
# ipython
|
||||
propcache==0.4.1
|
||||
# via
|
||||
# aiohttp
|
||||
# yarl
|
||||
protobuf==6.31.1
|
||||
protobuf==6.31.0
|
||||
# via
|
||||
# dm-control
|
||||
# grpcio-tools
|
||||
@@ -568,7 +550,7 @@ protobuf==6.31.1
|
||||
# tensorboard
|
||||
# tensorboardx
|
||||
# wandb
|
||||
psutil==7.2.2
|
||||
psutil==7.1.1
|
||||
# via
|
||||
# accelerate
|
||||
# imageio
|
||||
@@ -578,17 +560,17 @@ ptyprocess==0.7.0
|
||||
# via pexpect
|
||||
pure-eval==0.2.3
|
||||
# via stack-data
|
||||
pyarrow==23.0.1
|
||||
pyarrow==21.0.0
|
||||
# via
|
||||
# datasets
|
||||
# rerun-sdk
|
||||
pycparser==3.0
|
||||
pycparser==2.23
|
||||
# via cffi
|
||||
pydantic==2.12.5
|
||||
pydantic==2.12.3
|
||||
# via
|
||||
# fastapi
|
||||
# wandb
|
||||
pydantic-core==2.41.5
|
||||
pydantic-core==2.41.4
|
||||
# via pydantic
|
||||
pygame==2.6.1
|
||||
# via
|
||||
@@ -598,14 +580,12 @@ pygame==2.6.1
|
||||
pygments==2.19.2
|
||||
# via
|
||||
# ipython
|
||||
# ipython-pygments-lexers
|
||||
# pytest
|
||||
# rich
|
||||
pymunk==6.11.1
|
||||
# via
|
||||
# gym-pusht
|
||||
# lerobot
|
||||
pyngrok==7.5.1
|
||||
pyngrok==7.4.1
|
||||
# via meshcat
|
||||
pynput==1.8.1
|
||||
# via
|
||||
@@ -615,7 +595,7 @@ pyopengl==3.1.10
|
||||
# via
|
||||
# dm-control
|
||||
# mujoco
|
||||
pyparsing==3.3.2
|
||||
pyparsing==3.2.5
|
||||
# via
|
||||
# dm-control
|
||||
# matplotlib
|
||||
@@ -641,16 +621,13 @@ pytest-timeout==2.4.0
|
||||
# via lerobot
|
||||
python-dateutil==2.9.0.post0
|
||||
# via
|
||||
# faker
|
||||
# matplotlib
|
||||
# pandas
|
||||
python-discovery==1.1.1
|
||||
# via virtualenv
|
||||
python-dotenv==1.2.2
|
||||
python-dotenv==1.1.1
|
||||
# via uvicorn
|
||||
python-xlib==0.33
|
||||
# via pynput
|
||||
pytz==2026.1.post1
|
||||
pytz==2025.2
|
||||
# via pandas
|
||||
pyyaml==6.0.3
|
||||
# via
|
||||
@@ -665,6 +642,7 @@ pyyaml==6.0.3
|
||||
# pre-commit
|
||||
# pyngrok
|
||||
# pyyaml-include
|
||||
# timm
|
||||
# transformers
|
||||
# uvicorn
|
||||
# wandb
|
||||
@@ -674,9 +652,7 @@ pyzmq==27.1.0
|
||||
# via
|
||||
# lerobot
|
||||
# meshcat
|
||||
qwen-vl-utils==0.0.14
|
||||
# via lerobot
|
||||
reachy2-sdk==1.0.15
|
||||
reachy2-sdk==1.0.14
|
||||
# via lerobot
|
||||
reachy2-sdk-api==1.0.21
|
||||
# via reachy2-sdk
|
||||
@@ -684,7 +660,7 @@ referencing==0.37.0
|
||||
# via
|
||||
# jsonschema
|
||||
# jsonschema-specifications
|
||||
regex==2026.2.28
|
||||
regex==2025.10.23
|
||||
# via
|
||||
# diffusers
|
||||
# transformers
|
||||
@@ -693,62 +669,60 @@ requests==2.32.5
|
||||
# datasets
|
||||
# diffusers
|
||||
# dm-control
|
||||
# qwen-vl-utils
|
||||
# huggingface-hub
|
||||
# teleop
|
||||
# transformers
|
||||
# wandb
|
||||
rerun-sdk==0.26.2
|
||||
rerun-sdk==0.26.1
|
||||
# via lerobot
|
||||
rhoban-cmeel-jsoncpp==1.9.4.9
|
||||
# via placo
|
||||
rich==14.3.3
|
||||
# via typer
|
||||
robomimic==0.2.0
|
||||
# via hf-libero
|
||||
# via libero
|
||||
robosuite==1.4.0
|
||||
# via hf-libero
|
||||
rpds-py==0.30.0
|
||||
# via libero
|
||||
rpds-py==0.28.0
|
||||
# via
|
||||
# jsonschema
|
||||
# referencing
|
||||
safetensors==0.7.0
|
||||
safetensors==0.6.2
|
||||
# via
|
||||
# accelerate
|
||||
# diffusers
|
||||
# lerobot
|
||||
# peft
|
||||
# timm
|
||||
# transformers
|
||||
scikit-image==0.25.2
|
||||
# via
|
||||
# gym-pusht
|
||||
# lerobot
|
||||
scipy==1.17.1
|
||||
scipy==1.15.3
|
||||
# via
|
||||
# dm-control
|
||||
# lerobot
|
||||
# metaworld
|
||||
# robosuite
|
||||
# scikit-image
|
||||
# torchdiffeq
|
||||
sentry-sdk==2.54.0
|
||||
sentry-sdk==2.42.1
|
||||
# via wandb
|
||||
shapely==2.1.2
|
||||
# via gym-pusht
|
||||
shellingham==1.5.4
|
||||
# via typer
|
||||
six==1.17.0
|
||||
# via
|
||||
# pynput
|
||||
# python-dateutil
|
||||
# python-xlib
|
||||
smmap==5.0.3
|
||||
smmap==5.0.2
|
||||
# via gitdb
|
||||
sniffio==1.3.1
|
||||
# via anyio
|
||||
stack-data==0.6.3
|
||||
# via ipython
|
||||
starlette==0.52.1
|
||||
starlette==0.48.0
|
||||
# via fastapi
|
||||
sympy==1.14.0
|
||||
# via torch
|
||||
teleop==0.1.4
|
||||
teleop==0.1.2
|
||||
# via lerobot
|
||||
tensorboard==2.20.0
|
||||
# via robomimic
|
||||
@@ -756,38 +730,46 @@ tensorboard-data-server==0.7.2
|
||||
# via tensorboard
|
||||
tensorboardx==2.6.4
|
||||
# via robomimic
|
||||
termcolor==3.3.0
|
||||
termcolor==3.1.0
|
||||
# via
|
||||
# lerobot
|
||||
# robomimic
|
||||
thop==0.1.1.post2209072238
|
||||
# via hf-libero
|
||||
tifffile==2026.3.3
|
||||
# via libero
|
||||
tifffile==2025.5.10
|
||||
# via scikit-image
|
||||
tokenizers==0.22.2
|
||||
timm==1.0.20
|
||||
# via lerobot
|
||||
tokenizers==0.22.1
|
||||
# via transformers
|
||||
toml==0.10.2
|
||||
# via draccus
|
||||
torch==2.10.0
|
||||
tomli==2.3.0
|
||||
# via
|
||||
# cmeel
|
||||
# coverage
|
||||
# jupytext
|
||||
# pytest
|
||||
torch==2.7.1
|
||||
# via
|
||||
# accelerate
|
||||
# flash-attn
|
||||
# lerobot
|
||||
# peft
|
||||
# robomimic
|
||||
# thop
|
||||
# torchdiffeq
|
||||
# timm
|
||||
# torchvision
|
||||
torchcodec==0.10.0
|
||||
torchcodec==0.5
|
||||
# via lerobot
|
||||
torchdiffeq==0.2.5
|
||||
# via lerobot
|
||||
torchvision==0.25.0
|
||||
torchvision==0.22.1
|
||||
# via
|
||||
# lerobot
|
||||
# robomimic
|
||||
tornado==6.5.4
|
||||
# timm
|
||||
tornado==6.5.2
|
||||
# via meshcat
|
||||
tqdm==4.67.3
|
||||
tqdm==4.67.1
|
||||
# via
|
||||
# datasets
|
||||
# dm-control
|
||||
@@ -801,29 +783,26 @@ traitlets==5.14.3
|
||||
# jupyter-core
|
||||
# matplotlib-inline
|
||||
# nbformat
|
||||
transformers==5.3.0
|
||||
transformers==4.57.1
|
||||
# via
|
||||
# hf-libero
|
||||
# lerobot
|
||||
# libero
|
||||
# peft
|
||||
transforms3d==0.4.2
|
||||
# via teleop
|
||||
triton==3.6.0
|
||||
triton==3.3.1
|
||||
# via torch
|
||||
typer==0.24.1
|
||||
# via
|
||||
# huggingface-hub
|
||||
# transformers
|
||||
typing-extensions==4.15.0
|
||||
# via
|
||||
# aiosignal
|
||||
# anyio
|
||||
# etils
|
||||
# faker
|
||||
# exceptiongroup
|
||||
# fastapi
|
||||
# gymnasium
|
||||
# huggingface-hub
|
||||
# mypy
|
||||
# ipython
|
||||
# multidict
|
||||
# pydantic
|
||||
# pydantic-core
|
||||
# referencing
|
||||
@@ -832,46 +811,46 @@ typing-extensions==4.15.0
|
||||
# torch
|
||||
# typing-inspect
|
||||
# typing-inspection
|
||||
# uvicorn
|
||||
# virtualenv
|
||||
# wandb
|
||||
typing-inspect==0.9.0
|
||||
# via draccus
|
||||
typing-inspection==0.4.2
|
||||
# via
|
||||
# fastapi
|
||||
# pydantic
|
||||
tzdata==2025.3
|
||||
# via pydantic
|
||||
tzdata==2025.2
|
||||
# via pandas
|
||||
u-msgpack-python==2.8.0
|
||||
# via meshcat
|
||||
urllib3==2.6.3
|
||||
urllib3==2.5.0
|
||||
# via
|
||||
# requests
|
||||
# sentry-sdk
|
||||
uvicorn[standard]==0.41.0
|
||||
uvicorn[standard]==0.38.0
|
||||
# via teleop
|
||||
uvloop==0.22.1
|
||||
# via uvicorn
|
||||
virtualenv==21.1.0
|
||||
virtualenv==20.35.3
|
||||
# via pre-commit
|
||||
wandb==0.24.2
|
||||
wandb==0.21.4
|
||||
# via
|
||||
# hf-libero
|
||||
# lerobot
|
||||
# libero
|
||||
watchfiles==1.1.1
|
||||
# via uvicorn
|
||||
wcwidth==0.6.0
|
||||
wcwidth==0.2.14
|
||||
# via prompt-toolkit
|
||||
websocket-client==1.9.0
|
||||
# via teleop
|
||||
websockets==16.0
|
||||
websockets==15.0.1
|
||||
# via uvicorn
|
||||
werkzeug==3.1.6
|
||||
werkzeug==3.1.3
|
||||
# via tensorboard
|
||||
wrapt==2.1.2
|
||||
wrapt==2.0.0
|
||||
# via dm-tree
|
||||
xxhash==3.6.0
|
||||
# via datasets
|
||||
yarl==1.23.0
|
||||
yarl==1.22.0
|
||||
# via aiohttp
|
||||
zipp==3.23.0
|
||||
# via
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
# requirements.in
|
||||
|
||||
# requirements-macos.txt was generated on macOS and is platform-specific (macOS 26.3.1 25D2128 arm64).
|
||||
# Darwin MacBook-Pro.local 25.3.0 Darwin Kernel Version 25.3.0: Wed Jan 28 20:54:55 PST 2026; root:xnu-12377.91.3~2/RELEASE_ARM64_T8132 arm64
|
||||
# requirements-macos.txt was generated on macOS and is platform-specific (macOS 26.0.1 25A362 arm64).
|
||||
# Darwin MacBook-Pro.local 25.0.0 Darwin Kernel Version 25.0.0: Wed Sep 17 21:42:08 PDT 2025; root:xnu-12377.1.9~141/RELEASE_ARM64_T8132 arm64
|
||||
|
||||
# requirements-ubuntu.txt was generated on Linux and is platform-specific (Ubuntu 24.04.4 LTS x86_64).
|
||||
# Linux lerobot-linux 6.17.0-14-generic #14~24.04.1-Ubuntu SMP PREEMPT_DYNAMIC Thu Jan 15 15:52:10 UTC 2 x86_64 x86_64 x86_64 GNU/Linux
|
||||
# requirements-ubuntu.txt was generated on Linux and is platform-specific (Ubuntu 24.04.3 LTS x86_64).
|
||||
# Linux mlerobot-linux 6.14.0-33-generic #33~24.04.1-Ubuntu SMP PREEMPT_DYNAMIC Fri Sep 19 17:02:30 UTC 2 x86_64 x86_64 x86_64 GNU/Linux
|
||||
|
||||
-e .[all]
|
||||
|
||||
@@ -23,7 +23,7 @@ from typing import Any
|
||||
import torch
|
||||
|
||||
from lerobot.configs.types import PolicyFeature
|
||||
from lerobot.datasets.feature_utils import build_dataset_frame, hw_to_dataset_features
|
||||
from lerobot.datasets.utils import build_dataset_frame, hw_to_dataset_features
|
||||
|
||||
# NOTE: Configs need to be loaded for the client to be able to instantiate the policy config
|
||||
from lerobot.policies import ( # noqa: F401
|
||||
|
||||
@@ -39,13 +39,15 @@ import grpc
|
||||
import torch
|
||||
|
||||
from lerobot.policies.factory import get_policy_class, make_pre_post_processors
|
||||
from lerobot.processor import PolicyProcessorPipeline
|
||||
from lerobot.processor import (
|
||||
PolicyAction,
|
||||
PolicyProcessorPipeline,
|
||||
)
|
||||
from lerobot.transport import (
|
||||
services_pb2, # type: ignore
|
||||
services_pb2_grpc, # type: ignore
|
||||
)
|
||||
from lerobot.transport.utils import receive_bytes_in_chunks
|
||||
from lerobot.types import PolicyAction
|
||||
|
||||
from .configs import PolicyServerConfig
|
||||
from .constants import SUPPORTED_POLICIES
|
||||
|
||||
@@ -49,14 +49,9 @@ import torch
|
||||
|
||||
from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig # noqa: F401
|
||||
from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig # noqa: F401
|
||||
from lerobot.robots import ( # noqa: F401
|
||||
Robot,
|
||||
RobotConfig,
|
||||
bi_so_follower,
|
||||
koch_follower,
|
||||
from lerobot.robots import (
|
||||
RobotConfig, # noqa: F401
|
||||
make_robot_from_config,
|
||||
omx_follower,
|
||||
so_follower,
|
||||
)
|
||||
from lerobot.transport import (
|
||||
services_pb2, # type: ignore
|
||||
|
||||
@@ -181,7 +181,7 @@ class ZMQCamera(Camera):
|
||||
try:
|
||||
message = self.socket.recv_string()
|
||||
except Exception as e:
|
||||
# zmq is lazy-imported in connect(), so check by name to avoid a top-level import
|
||||
# Check for ZMQ timeout (EAGAIN/Again) without requiring global zmq import
|
||||
if type(e).__name__ == "Again":
|
||||
raise TimeoutError(f"{self} timeout after {self.timeout_ms}ms") from e
|
||||
raise
|
||||
|
||||
@@ -23,7 +23,6 @@ import base64
|
||||
import contextlib
|
||||
import json
|
||||
import logging
|
||||
import threading
|
||||
import time
|
||||
from collections import deque
|
||||
|
||||
@@ -43,57 +42,10 @@ def encode_image(image: np.ndarray, quality: int = 80) -> str:
|
||||
return base64.b64encode(buffer).decode("utf-8")
|
||||
|
||||
|
||||
class CameraCaptureThread:
|
||||
"""Background thread that continuously captures and encodes frames from a camera."""
|
||||
|
||||
def __init__(self, camera: OpenCVCamera, name: str):
|
||||
self.camera = camera
|
||||
self.name = name
|
||||
self.latest_encoded: str | None = None # Pre-encoded JPEG as base64
|
||||
self.latest_timestamp: float = 0.0
|
||||
self.frame_lock = threading.Lock()
|
||||
self.running = False
|
||||
self.thread: threading.Thread | None = None
|
||||
|
||||
def start(self):
|
||||
"""Start the capture thread."""
|
||||
self.running = True
|
||||
self.thread = threading.Thread(target=self._capture_loop, daemon=True)
|
||||
self.thread.start()
|
||||
|
||||
def stop(self):
|
||||
"""Stop the capture thread."""
|
||||
self.running = False
|
||||
if self.thread:
|
||||
self.thread.join(timeout=1.0)
|
||||
|
||||
def _capture_loop(self):
|
||||
"""Continuously capture and encode frames at the camera's native rate."""
|
||||
while self.running:
|
||||
try:
|
||||
frame = self.camera.read() # Blocks at camera's native rate
|
||||
timestamp = time.time()
|
||||
# Encode immediately in capture thread (this is the slow part)
|
||||
encoded = encode_image(frame)
|
||||
with self.frame_lock:
|
||||
self.latest_encoded = encoded
|
||||
self.latest_timestamp = timestamp
|
||||
except Exception as e:
|
||||
logger.warning(f"Camera {self.name} capture error: {e}")
|
||||
time.sleep(0.01)
|
||||
|
||||
def get_latest(self) -> tuple[str | None, float]:
|
||||
"""Get the latest encoded frame and its timestamp."""
|
||||
with self.frame_lock:
|
||||
return self.latest_encoded, self.latest_timestamp
|
||||
|
||||
|
||||
class ImageServer:
|
||||
def __init__(self, config: dict, port: int = 5555):
|
||||
# fps controls the publish loop rate (how often frames are sent over ZMQ), not the camera capture rate
|
||||
self.fps = config.get("fps", 30)
|
||||
self.cameras: dict[str, OpenCVCamera] = {}
|
||||
self.capture_threads: dict[str, CameraCaptureThread] = {}
|
||||
|
||||
for name, cfg in config.get("cameras", {}).items():
|
||||
shape = cfg.get("shape", [480, 640])
|
||||
@@ -109,10 +61,6 @@ class ImageServer:
|
||||
self.cameras[name] = camera
|
||||
logger.info(f"Camera {name}: {shape[1]}x{shape[0]}")
|
||||
|
||||
# Create capture thread for this camera
|
||||
capture_thread = CameraCaptureThread(camera, name)
|
||||
self.capture_threads[name] = capture_thread
|
||||
|
||||
# ZMQ PUB socket
|
||||
self.context = zmq.Context()
|
||||
self.socket = self.context.socket(zmq.PUB)
|
||||
@@ -125,18 +73,6 @@ class ImageServer:
|
||||
def run(self):
|
||||
frame_count = 0
|
||||
frame_times = deque(maxlen=60)
|
||||
last_published_ts: dict[str, float] = {}
|
||||
|
||||
# Start all capture threads
|
||||
for capture_thread in self.capture_threads.values():
|
||||
capture_thread.start()
|
||||
|
||||
# Wait for first frames to be captured and encoded
|
||||
logger.info("Waiting for cameras to start capturing...")
|
||||
for name, capture_thread in self.capture_threads.items():
|
||||
while capture_thread.get_latest()[0] is None:
|
||||
time.sleep(0.01)
|
||||
logger.info(f"Camera {name} ready (capture + encode in background)")
|
||||
|
||||
try:
|
||||
while True:
|
||||
@@ -144,12 +80,10 @@ class ImageServer:
|
||||
|
||||
# Build message
|
||||
message = {"timestamps": {}, "images": {}}
|
||||
for name, capture_thread in self.capture_threads.items():
|
||||
encoded, timestamp = capture_thread.get_latest()
|
||||
if encoded is not None and timestamp > last_published_ts.get(name, 0.0):
|
||||
message["timestamps"][name] = timestamp
|
||||
message["images"][name] = encoded
|
||||
last_published_ts[name] = timestamp
|
||||
for name, cam in self.cameras.items():
|
||||
frame = cam.read() # Returns RGB
|
||||
message["timestamps"][name] = time.time()
|
||||
message["images"][name] = encode_image(frame)
|
||||
|
||||
# Send as JSON string (suppress if buffer full)
|
||||
with contextlib.suppress(zmq.Again):
|
||||
@@ -168,8 +102,6 @@ class ImageServer:
|
||||
except KeyboardInterrupt:
|
||||
pass
|
||||
finally:
|
||||
for capture_thread in self.capture_threads.values():
|
||||
capture_thread.stop()
|
||||
for cam in self.cameras.values():
|
||||
cam.disconnect()
|
||||
self.socket.close()
|
||||
|
||||
@@ -36,16 +36,6 @@ class DatasetConfig:
|
||||
video_backend: str = field(default_factory=get_safe_default_codec)
|
||||
streaming: bool = False
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
if self.episodes is not None:
|
||||
if any(ep < 0 for ep in self.episodes):
|
||||
raise ValueError(
|
||||
f"Episode indices must be non-negative, got: {[ep for ep in self.episodes if ep < 0]}"
|
||||
)
|
||||
if len(self.episodes) != len(set(self.episodes)):
|
||||
duplicates = sorted({ep for ep in self.episodes if self.episodes.count(ep) > 1})
|
||||
raise ValueError(f"Episode indices contain duplicates: {duplicates}")
|
||||
|
||||
|
||||
@dataclass
|
||||
class WandBConfig:
|
||||
@@ -57,7 +47,6 @@ class WandBConfig:
|
||||
notes: str | None = None
|
||||
run_id: str | None = None
|
||||
mode: str | None = None # Allowed values: 'online', 'offline' 'disabled'. Defaults to 'online'
|
||||
add_tags: bool = True # If True, save configuration as tags in the WandB run.
|
||||
|
||||
|
||||
@dataclass
|
||||
|
||||
@@ -30,8 +30,8 @@ from lerobot.configs.types import FeatureType, PolicyFeature
|
||||
from lerobot.optim.optimizers import OptimizerConfig
|
||||
from lerobot.optim.schedulers import LRSchedulerConfig
|
||||
from lerobot.utils.constants import ACTION, OBS_STATE
|
||||
from lerobot.utils.device_utils import auto_select_torch_device, is_amp_available, is_torch_device_available
|
||||
from lerobot.utils.hub import HubMixin
|
||||
from lerobot.utils.utils import auto_select_torch_device, is_amp_available, is_torch_device_available
|
||||
|
||||
T = TypeVar("T", bound="PreTrainedConfig")
|
||||
logger = getLogger(__name__)
|
||||
|
||||
@@ -50,9 +50,6 @@ class TrainPipelineConfig(HubMixin):
|
||||
# `seed` is used for training (eg: model initialization, dataset shuffling)
|
||||
# AND for the evaluation environments.
|
||||
seed: int | None = 1000
|
||||
# Set to True to use deterministic cuDNN algorithms for reproducibility.
|
||||
# This disables cudnn.benchmark and may reduce training speed by ~10-20 percent.
|
||||
cudnn_deterministic: bool = False
|
||||
# Number of workers for the dataloader.
|
||||
num_workers: int = 4
|
||||
batch_size: int = 8
|
||||
|
||||
@@ -746,8 +746,7 @@ def save_annotations_to_dataset(
|
||||
dataset_path: Path, annotations: dict[int, SubtaskAnnotation], fps: int, prefix: str = "sparse"
|
||||
):
|
||||
"""Save annotations to LeRobot dataset parquet format."""
|
||||
from lerobot.datasets.io_utils import load_episodes
|
||||
from lerobot.datasets.utils import DEFAULT_EPISODES_PATH
|
||||
from lerobot.datasets.utils import DEFAULT_EPISODES_PATH, load_episodes
|
||||
|
||||
episodes_dataset = load_episodes(dataset_path)
|
||||
if not episodes_dataset or len(episodes_dataset) == 0:
|
||||
@@ -841,7 +840,7 @@ def generate_auto_sparse_annotations(
|
||||
|
||||
def load_annotations_from_dataset(dataset_path: Path, prefix: str = "sparse") -> dict[int, SubtaskAnnotation]:
|
||||
"""Load annotations from LeRobot dataset parquet files."""
|
||||
from lerobot.datasets.io_utils import load_episodes
|
||||
from lerobot.datasets.utils import load_episodes
|
||||
|
||||
episodes_dataset = load_episodes(dataset_path)
|
||||
if not episodes_dataset or len(episodes_dataset) == 0:
|
||||
|
||||
@@ -24,16 +24,7 @@ import pandas as pd
|
||||
import tqdm
|
||||
|
||||
from lerobot.datasets.compute_stats import aggregate_stats
|
||||
from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
|
||||
from lerobot.datasets.feature_utils import get_hf_features_from_features
|
||||
from lerobot.datasets.io_utils import (
|
||||
get_file_size_in_mb,
|
||||
get_parquet_file_size_in_mb,
|
||||
to_parquet_with_hf_images,
|
||||
write_info,
|
||||
write_stats,
|
||||
write_tasks,
|
||||
)
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata
|
||||
from lerobot.datasets.utils import (
|
||||
DEFAULT_CHUNK_SIZE,
|
||||
DEFAULT_DATA_FILE_SIZE_IN_MB,
|
||||
@@ -41,7 +32,14 @@ from lerobot.datasets.utils import (
|
||||
DEFAULT_EPISODES_PATH,
|
||||
DEFAULT_VIDEO_FILE_SIZE_IN_MB,
|
||||
DEFAULT_VIDEO_PATH,
|
||||
get_file_size_in_mb,
|
||||
get_hf_features_from_features,
|
||||
get_parquet_file_size_in_mb,
|
||||
to_parquet_with_hf_images,
|
||||
update_chunk_file_indices,
|
||||
write_info,
|
||||
write_stats,
|
||||
write_tasks,
|
||||
)
|
||||
from lerobot.datasets.video_utils import concatenate_video_files, get_video_duration_in_s
|
||||
|
||||
|
||||
56
src/lerobot/datasets/backward_compatibility.py
Normal file
56
src/lerobot/datasets/backward_compatibility.py
Normal file
@@ -0,0 +1,56 @@
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import packaging.version
|
||||
|
||||
V30_MESSAGE = """
|
||||
The dataset you requested ({repo_id}) is in {version} format.
|
||||
|
||||
We introduced a new format since v3.0 which is not backward compatible with v2.1.
|
||||
Please, update your dataset to the new format using this command:
|
||||
```
|
||||
python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 --repo-id={repo_id}
|
||||
```
|
||||
|
||||
If you already have a converted version uploaded to the hub, then this error might be because of
|
||||
an older version in your local cache. Consider deleting the cached version and retrying.
|
||||
|
||||
If you encounter a problem, contact LeRobot maintainers on [Discord](https://discord.com/invite/s3KuuzsPFb)
|
||||
or open an [issue on GitHub](https://github.com/huggingface/lerobot/issues/new/choose).
|
||||
"""
|
||||
|
||||
FUTURE_MESSAGE = """
|
||||
The dataset you requested ({repo_id}) is only available in {version} format.
|
||||
As we cannot ensure forward compatibility with it, please update your current version of lerobot.
|
||||
"""
|
||||
|
||||
|
||||
class CompatibilityError(Exception): ...
|
||||
|
||||
|
||||
class BackwardCompatibilityError(CompatibilityError):
|
||||
def __init__(self, repo_id: str, version: packaging.version.Version):
|
||||
if version.major == 2 and version.minor == 1:
|
||||
message = V30_MESSAGE.format(repo_id=repo_id, version=version)
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
"Contact the maintainer on [Discord](https://discord.com/invite/s3KuuzsPFb)."
|
||||
)
|
||||
super().__init__(message)
|
||||
|
||||
|
||||
class ForwardCompatibilityError(CompatibilityError):
|
||||
def __init__(self, repo_id: str, version: packaging.version.Version):
|
||||
message = FUTURE_MESSAGE.format(repo_id=repo_id, version=version)
|
||||
super().__init__(message)
|
||||
@@ -15,7 +15,7 @@
|
||||
# limitations under the License.
|
||||
import numpy as np
|
||||
|
||||
from lerobot.datasets.io_utils import load_image_as_numpy
|
||||
from lerobot.datasets.utils import load_image_as_numpy
|
||||
|
||||
DEFAULT_QUANTILES = [0.01, 0.10, 0.50, 0.90, 0.99]
|
||||
|
||||
|
||||
@@ -1,517 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import packaging.version
|
||||
import pandas as pd
|
||||
import pyarrow as pa
|
||||
import pyarrow.parquet as pq
|
||||
from huggingface_hub import snapshot_download
|
||||
|
||||
from lerobot.datasets.compute_stats import aggregate_stats
|
||||
from lerobot.datasets.feature_utils import _validate_feature_names, create_empty_dataset_info
|
||||
from lerobot.datasets.io_utils import (
|
||||
get_file_size_in_mb,
|
||||
load_episodes,
|
||||
load_info,
|
||||
load_stats,
|
||||
load_subtasks,
|
||||
load_tasks,
|
||||
write_info,
|
||||
write_json,
|
||||
write_stats,
|
||||
write_tasks,
|
||||
)
|
||||
from lerobot.datasets.utils import (
|
||||
DEFAULT_EPISODES_PATH,
|
||||
DEFAULT_FEATURES,
|
||||
INFO_PATH,
|
||||
check_version_compatibility,
|
||||
flatten_dict,
|
||||
get_safe_version,
|
||||
is_valid_version,
|
||||
update_chunk_file_indices,
|
||||
)
|
||||
from lerobot.datasets.video_utils import get_video_info
|
||||
from lerobot.utils.constants import HF_LEROBOT_HOME
|
||||
|
||||
CODEBASE_VERSION = "v3.0"
|
||||
|
||||
|
||||
class LeRobotDatasetMetadata:
|
||||
def __init__(
|
||||
self,
|
||||
repo_id: str,
|
||||
root: str | Path | None = None,
|
||||
revision: str | None = None,
|
||||
force_cache_sync: bool = False,
|
||||
metadata_buffer_size: int = 10,
|
||||
):
|
||||
self.repo_id = repo_id
|
||||
self.revision = revision if revision else CODEBASE_VERSION
|
||||
self.root = Path(root) if root is not None else HF_LEROBOT_HOME / repo_id
|
||||
self.writer = None
|
||||
self.latest_episode = None
|
||||
self.metadata_buffer: list[dict] = []
|
||||
self.metadata_buffer_size = metadata_buffer_size
|
||||
|
||||
try:
|
||||
if force_cache_sync:
|
||||
raise FileNotFoundError
|
||||
self.load_metadata()
|
||||
except (FileNotFoundError, NotADirectoryError):
|
||||
if is_valid_version(self.revision):
|
||||
self.revision = get_safe_version(self.repo_id, self.revision)
|
||||
|
||||
(self.root / "meta").mkdir(exist_ok=True, parents=True)
|
||||
self.pull_from_repo(allow_patterns="meta/")
|
||||
self.load_metadata()
|
||||
|
||||
def _flush_metadata_buffer(self) -> None:
|
||||
"""Write all buffered episode metadata to parquet file."""
|
||||
if not hasattr(self, "metadata_buffer") or len(self.metadata_buffer) == 0:
|
||||
return
|
||||
|
||||
combined_dict = {}
|
||||
for episode_dict in self.metadata_buffer:
|
||||
for key, value in episode_dict.items():
|
||||
if key not in combined_dict:
|
||||
combined_dict[key] = []
|
||||
# Extract value and serialize numpy arrays
|
||||
# because PyArrow's from_pydict function doesn't support numpy arrays
|
||||
val = value[0] if isinstance(value, list) else value
|
||||
combined_dict[key].append(val.tolist() if isinstance(val, np.ndarray) else val)
|
||||
|
||||
first_ep = self.metadata_buffer[0]
|
||||
chunk_idx = first_ep["meta/episodes/chunk_index"][0]
|
||||
file_idx = first_ep["meta/episodes/file_index"][0]
|
||||
|
||||
table = pa.Table.from_pydict(combined_dict)
|
||||
|
||||
if not self.writer:
|
||||
path = Path(self.root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx))
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
self.writer = pq.ParquetWriter(
|
||||
path, schema=table.schema, compression="snappy", use_dictionary=True
|
||||
)
|
||||
|
||||
self.writer.write_table(table)
|
||||
|
||||
self.latest_episode = self.metadata_buffer[-1]
|
||||
self.metadata_buffer.clear()
|
||||
|
||||
def _close_writer(self) -> None:
|
||||
"""Close and cleanup the parquet writer if it exists."""
|
||||
self._flush_metadata_buffer()
|
||||
|
||||
writer = getattr(self, "writer", None)
|
||||
if writer is not None:
|
||||
writer.close()
|
||||
self.writer = None
|
||||
|
||||
def __del__(self):
|
||||
"""
|
||||
Trust the user to call .finalize() but as an added safety check call the parquet writer to stop when calling the destructor
|
||||
"""
|
||||
self._close_writer()
|
||||
|
||||
def load_metadata(self):
|
||||
self.info = load_info(self.root)
|
||||
check_version_compatibility(self.repo_id, self._version, CODEBASE_VERSION)
|
||||
self.tasks = load_tasks(self.root)
|
||||
self.subtasks = load_subtasks(self.root)
|
||||
self.episodes = load_episodes(self.root)
|
||||
self.stats = load_stats(self.root)
|
||||
|
||||
def pull_from_repo(
|
||||
self,
|
||||
allow_patterns: list[str] | str | None = None,
|
||||
ignore_patterns: list[str] | str | None = None,
|
||||
) -> None:
|
||||
snapshot_download(
|
||||
self.repo_id,
|
||||
repo_type="dataset",
|
||||
revision=self.revision,
|
||||
local_dir=self.root,
|
||||
allow_patterns=allow_patterns,
|
||||
ignore_patterns=ignore_patterns,
|
||||
)
|
||||
|
||||
@property
|
||||
def url_root(self) -> str:
|
||||
return f"hf://datasets/{self.repo_id}"
|
||||
|
||||
@property
|
||||
def _version(self) -> packaging.version.Version:
|
||||
"""Codebase version used to create this dataset."""
|
||||
return packaging.version.parse(self.info["codebase_version"])
|
||||
|
||||
def get_data_file_path(self, ep_index: int) -> Path:
|
||||
if self.episodes is None:
|
||||
self.episodes = load_episodes(self.root)
|
||||
if ep_index >= len(self.episodes):
|
||||
raise IndexError(
|
||||
f"Episode index {ep_index} out of range. Episodes: {len(self.episodes) if self.episodes else 0}"
|
||||
)
|
||||
ep = self.episodes[ep_index]
|
||||
chunk_idx = ep["data/chunk_index"]
|
||||
file_idx = ep["data/file_index"]
|
||||
fpath = self.data_path.format(chunk_index=chunk_idx, file_index=file_idx)
|
||||
return Path(fpath)
|
||||
|
||||
def get_video_file_path(self, ep_index: int, vid_key: str) -> Path:
|
||||
if self.episodes is None:
|
||||
self.episodes = load_episodes(self.root)
|
||||
if ep_index >= len(self.episodes):
|
||||
raise IndexError(
|
||||
f"Episode index {ep_index} out of range. Episodes: {len(self.episodes) if self.episodes else 0}"
|
||||
)
|
||||
ep = self.episodes[ep_index]
|
||||
chunk_idx = ep[f"videos/{vid_key}/chunk_index"]
|
||||
file_idx = ep[f"videos/{vid_key}/file_index"]
|
||||
fpath = self.video_path.format(video_key=vid_key, chunk_index=chunk_idx, file_index=file_idx)
|
||||
return Path(fpath)
|
||||
|
||||
@property
|
||||
def data_path(self) -> str:
|
||||
"""Formattable string for the parquet files."""
|
||||
return self.info["data_path"]
|
||||
|
||||
@property
|
||||
def video_path(self) -> str | None:
|
||||
"""Formattable string for the video files."""
|
||||
return self.info["video_path"]
|
||||
|
||||
@property
|
||||
def robot_type(self) -> str | None:
|
||||
"""Robot type used in recording this dataset."""
|
||||
return self.info["robot_type"]
|
||||
|
||||
@property
|
||||
def fps(self) -> int:
|
||||
"""Frames per second used during data collection."""
|
||||
return self.info["fps"]
|
||||
|
||||
@property
|
||||
def features(self) -> dict[str, dict]:
|
||||
"""All features contained in the dataset."""
|
||||
return self.info["features"]
|
||||
|
||||
@property
|
||||
def image_keys(self) -> list[str]:
|
||||
"""Keys to access visual modalities stored as images."""
|
||||
return [key for key, ft in self.features.items() if ft["dtype"] == "image"]
|
||||
|
||||
@property
|
||||
def video_keys(self) -> list[str]:
|
||||
"""Keys to access visual modalities stored as videos."""
|
||||
return [key for key, ft in self.features.items() if ft["dtype"] == "video"]
|
||||
|
||||
@property
|
||||
def camera_keys(self) -> list[str]:
|
||||
"""Keys to access visual modalities (regardless of their storage method)."""
|
||||
return [key for key, ft in self.features.items() if ft["dtype"] in ["video", "image"]]
|
||||
|
||||
@property
|
||||
def names(self) -> dict[str, list | dict]:
|
||||
"""Names of the various dimensions of vector modalities."""
|
||||
return {key: ft["names"] for key, ft in self.features.items()}
|
||||
|
||||
@property
|
||||
def shapes(self) -> dict:
|
||||
"""Shapes for the different features."""
|
||||
return {key: tuple(ft["shape"]) for key, ft in self.features.items()}
|
||||
|
||||
@property
|
||||
def total_episodes(self) -> int:
|
||||
"""Total number of episodes available."""
|
||||
return self.info["total_episodes"]
|
||||
|
||||
@property
|
||||
def total_frames(self) -> int:
|
||||
"""Total number of frames saved in this dataset."""
|
||||
return self.info["total_frames"]
|
||||
|
||||
@property
|
||||
def total_tasks(self) -> int:
|
||||
"""Total number of different tasks performed in this dataset."""
|
||||
return self.info["total_tasks"]
|
||||
|
||||
@property
|
||||
def chunks_size(self) -> int:
|
||||
"""Max number of files per chunk."""
|
||||
return self.info["chunks_size"]
|
||||
|
||||
@property
|
||||
def data_files_size_in_mb(self) -> int:
|
||||
"""Max size of data file in mega bytes."""
|
||||
return self.info["data_files_size_in_mb"]
|
||||
|
||||
@property
|
||||
def video_files_size_in_mb(self) -> int:
|
||||
"""Max size of video file in mega bytes."""
|
||||
return self.info["video_files_size_in_mb"]
|
||||
|
||||
def get_task_index(self, task: str) -> int | None:
|
||||
"""
|
||||
Given a task in natural language, returns its task_index if the task already exists in the dataset,
|
||||
otherwise return None.
|
||||
"""
|
||||
if task in self.tasks.index:
|
||||
return int(self.tasks.loc[task].task_index)
|
||||
else:
|
||||
return None
|
||||
|
||||
def save_episode_tasks(self, tasks: list[str]):
|
||||
if len(set(tasks)) != len(tasks):
|
||||
raise ValueError(f"Tasks are not unique: {tasks}")
|
||||
|
||||
if self.tasks is None:
|
||||
new_tasks = tasks
|
||||
task_indices = range(len(tasks))
|
||||
self.tasks = pd.DataFrame({"task_index": task_indices}, index=pd.Index(tasks, name="task"))
|
||||
else:
|
||||
new_tasks = [task for task in tasks if task not in self.tasks.index]
|
||||
new_task_indices = range(len(self.tasks), len(self.tasks) + len(new_tasks))
|
||||
for task_idx, task in zip(new_task_indices, new_tasks, strict=False):
|
||||
self.tasks.loc[task] = task_idx
|
||||
|
||||
if len(new_tasks) > 0:
|
||||
# Update on disk
|
||||
write_tasks(self.tasks, self.root)
|
||||
|
||||
def _save_episode_metadata(self, episode_dict: dict) -> None:
|
||||
"""Buffer episode metadata and write to parquet in batches for efficiency.
|
||||
|
||||
This function accumulates episode metadata in a buffer and flushes it when the buffer
|
||||
reaches the configured size. This reduces I/O overhead by writing multiple episodes
|
||||
at once instead of one row at a time.
|
||||
|
||||
Notes: We both need to update parquet files and HF dataset:
|
||||
- `pandas` loads parquet file in RAM
|
||||
- `datasets` relies on a memory mapping from pyarrow (no RAM). It either converts parquet files to a pyarrow cache on disk,
|
||||
or loads directly from pyarrow cache.
|
||||
"""
|
||||
# Convert to list format for each value
|
||||
episode_dict = {key: [value] for key, value in episode_dict.items()}
|
||||
num_frames = episode_dict["length"][0]
|
||||
|
||||
if self.latest_episode is None:
|
||||
# Initialize indices and frame count for a new dataset made of the first episode data
|
||||
chunk_idx, file_idx = 0, 0
|
||||
if self.episodes is not None and len(self.episodes) > 0:
|
||||
# It means we are resuming recording, so we need to load the latest episode
|
||||
# Update the indices to avoid overwriting the latest episode
|
||||
chunk_idx = self.episodes[-1]["meta/episodes/chunk_index"]
|
||||
file_idx = self.episodes[-1]["meta/episodes/file_index"]
|
||||
latest_num_frames = self.episodes[-1]["dataset_to_index"]
|
||||
episode_dict["dataset_from_index"] = [latest_num_frames]
|
||||
episode_dict["dataset_to_index"] = [latest_num_frames + num_frames]
|
||||
|
||||
# When resuming, move to the next file
|
||||
chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.chunks_size)
|
||||
else:
|
||||
episode_dict["dataset_from_index"] = [0]
|
||||
episode_dict["dataset_to_index"] = [num_frames]
|
||||
|
||||
episode_dict["meta/episodes/chunk_index"] = [chunk_idx]
|
||||
episode_dict["meta/episodes/file_index"] = [file_idx]
|
||||
else:
|
||||
chunk_idx = self.latest_episode["meta/episodes/chunk_index"][0]
|
||||
file_idx = self.latest_episode["meta/episodes/file_index"][0]
|
||||
|
||||
latest_path = (
|
||||
self.root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
|
||||
if self.writer is None
|
||||
else self.writer.where
|
||||
)
|
||||
|
||||
if Path(latest_path).exists():
|
||||
latest_size_in_mb = get_file_size_in_mb(Path(latest_path))
|
||||
latest_num_frames = self.latest_episode["episode_index"][0]
|
||||
|
||||
av_size_per_frame = latest_size_in_mb / latest_num_frames if latest_num_frames > 0 else 0.0
|
||||
|
||||
if latest_size_in_mb + av_size_per_frame * num_frames >= self.data_files_size_in_mb:
|
||||
# Size limit is reached, flush buffer and prepare new parquet file
|
||||
self._flush_metadata_buffer()
|
||||
chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.chunks_size)
|
||||
self._close_writer()
|
||||
|
||||
# Update the existing pandas dataframe with new row
|
||||
episode_dict["meta/episodes/chunk_index"] = [chunk_idx]
|
||||
episode_dict["meta/episodes/file_index"] = [file_idx]
|
||||
episode_dict["dataset_from_index"] = [self.latest_episode["dataset_to_index"][0]]
|
||||
episode_dict["dataset_to_index"] = [self.latest_episode["dataset_to_index"][0] + num_frames]
|
||||
|
||||
# Add to buffer
|
||||
self.metadata_buffer.append(episode_dict)
|
||||
self.latest_episode = episode_dict
|
||||
|
||||
if len(self.metadata_buffer) >= self.metadata_buffer_size:
|
||||
self._flush_metadata_buffer()
|
||||
|
||||
def save_episode(
|
||||
self,
|
||||
episode_index: int,
|
||||
episode_length: int,
|
||||
episode_tasks: list[str],
|
||||
episode_stats: dict[str, dict],
|
||||
episode_metadata: dict,
|
||||
) -> None:
|
||||
episode_dict = {
|
||||
"episode_index": episode_index,
|
||||
"tasks": episode_tasks,
|
||||
"length": episode_length,
|
||||
}
|
||||
episode_dict.update(episode_metadata)
|
||||
episode_dict.update(flatten_dict({"stats": episode_stats}))
|
||||
self._save_episode_metadata(episode_dict)
|
||||
|
||||
# Update info
|
||||
self.info["total_episodes"] += 1
|
||||
self.info["total_frames"] += episode_length
|
||||
self.info["total_tasks"] = len(self.tasks)
|
||||
self.info["splits"] = {"train": f"0:{self.info['total_episodes']}"}
|
||||
|
||||
write_info(self.info, self.root)
|
||||
|
||||
self.stats = aggregate_stats([self.stats, episode_stats]) if self.stats is not None else episode_stats
|
||||
write_stats(self.stats, self.root)
|
||||
|
||||
def update_video_info(self, video_key: str | None = None) -> None:
|
||||
"""
|
||||
Warning: this function writes info from first episode videos, implicitly assuming that all videos have
|
||||
been encoded the same way. Also, this means it assumes the first episode exists.
|
||||
"""
|
||||
if video_key is not None and video_key not in self.video_keys:
|
||||
raise ValueError(f"Video key {video_key} not found in dataset")
|
||||
|
||||
video_keys = [video_key] if video_key is not None else self.video_keys
|
||||
for key in video_keys:
|
||||
if not self.features[key].get("info", None):
|
||||
video_path = self.root / self.video_path.format(video_key=key, chunk_index=0, file_index=0)
|
||||
self.info["features"][key]["info"] = get_video_info(video_path)
|
||||
|
||||
def update_chunk_settings(
|
||||
self,
|
||||
chunks_size: int | None = None,
|
||||
data_files_size_in_mb: int | None = None,
|
||||
video_files_size_in_mb: int | None = None,
|
||||
) -> None:
|
||||
"""Update chunk and file size settings after dataset creation.
|
||||
|
||||
This allows users to customize storage organization without modifying the constructor.
|
||||
These settings control how episodes are chunked and how large files can grow before
|
||||
creating new ones.
|
||||
|
||||
Args:
|
||||
chunks_size: Maximum number of files per chunk directory. If None, keeps current value.
|
||||
data_files_size_in_mb: Maximum size for data parquet files in MB. If None, keeps current value.
|
||||
video_files_size_in_mb: Maximum size for video files in MB. If None, keeps current value.
|
||||
"""
|
||||
if chunks_size is not None:
|
||||
if chunks_size <= 0:
|
||||
raise ValueError(f"chunks_size must be positive, got {chunks_size}")
|
||||
self.info["chunks_size"] = chunks_size
|
||||
|
||||
if data_files_size_in_mb is not None:
|
||||
if data_files_size_in_mb <= 0:
|
||||
raise ValueError(f"data_files_size_in_mb must be positive, got {data_files_size_in_mb}")
|
||||
self.info["data_files_size_in_mb"] = data_files_size_in_mb
|
||||
|
||||
if video_files_size_in_mb is not None:
|
||||
if video_files_size_in_mb <= 0:
|
||||
raise ValueError(f"video_files_size_in_mb must be positive, got {video_files_size_in_mb}")
|
||||
self.info["video_files_size_in_mb"] = video_files_size_in_mb
|
||||
|
||||
# Update the info file on disk
|
||||
write_info(self.info, self.root)
|
||||
|
||||
def get_chunk_settings(self) -> dict[str, int]:
|
||||
"""Get current chunk and file size settings.
|
||||
|
||||
Returns:
|
||||
Dict containing chunks_size, data_files_size_in_mb, and video_files_size_in_mb.
|
||||
"""
|
||||
return {
|
||||
"chunks_size": self.chunks_size,
|
||||
"data_files_size_in_mb": self.data_files_size_in_mb,
|
||||
"video_files_size_in_mb": self.video_files_size_in_mb,
|
||||
}
|
||||
|
||||
def __repr__(self):
|
||||
feature_keys = list(self.features)
|
||||
return (
|
||||
f"{self.__class__.__name__}({{\n"
|
||||
f" Repository ID: '{self.repo_id}',\n"
|
||||
f" Total episodes: '{self.total_episodes}',\n"
|
||||
f" Total frames: '{self.total_frames}',\n"
|
||||
f" Features: '{feature_keys}',\n"
|
||||
"})',\n"
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def create(
|
||||
cls,
|
||||
repo_id: str,
|
||||
fps: int,
|
||||
features: dict,
|
||||
robot_type: str | None = None,
|
||||
root: str | Path | None = None,
|
||||
use_videos: bool = True,
|
||||
metadata_buffer_size: int = 10,
|
||||
chunks_size: int | None = None,
|
||||
data_files_size_in_mb: int | None = None,
|
||||
video_files_size_in_mb: int | None = None,
|
||||
) -> "LeRobotDatasetMetadata":
|
||||
"""Creates metadata for a LeRobotDataset."""
|
||||
obj = cls.__new__(cls)
|
||||
obj.repo_id = repo_id
|
||||
obj.root = Path(root) if root is not None else HF_LEROBOT_HOME / repo_id
|
||||
|
||||
obj.root.mkdir(parents=True, exist_ok=False)
|
||||
|
||||
features = {**features, **DEFAULT_FEATURES}
|
||||
_validate_feature_names(features)
|
||||
|
||||
obj.tasks = None
|
||||
obj.subtasks = None
|
||||
obj.episodes = None
|
||||
obj.stats = None
|
||||
obj.info = create_empty_dataset_info(
|
||||
CODEBASE_VERSION,
|
||||
fps,
|
||||
features,
|
||||
use_videos,
|
||||
robot_type,
|
||||
chunks_size,
|
||||
data_files_size_in_mb,
|
||||
video_files_size_in_mb,
|
||||
)
|
||||
if len(obj.video_keys) > 0 and not use_videos:
|
||||
raise ValueError(
|
||||
f"Features contain video keys {obj.video_keys}, but 'use_videos' is set to False. "
|
||||
"Either remove video features from the features dict, or set 'use_videos=True'."
|
||||
)
|
||||
write_json(obj.info, obj.root / INFO_PATH)
|
||||
obj.revision = None
|
||||
obj.writer = None
|
||||
obj.latest_episode = None
|
||||
obj.metadata_buffer = []
|
||||
obj.metadata_buffer_size = metadata_buffer_size
|
||||
return obj
|
||||
@@ -38,22 +38,19 @@ from tqdm import tqdm
|
||||
|
||||
from lerobot.datasets.aggregate import aggregate_datasets
|
||||
from lerobot.datasets.compute_stats import aggregate_stats
|
||||
from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
|
||||
from lerobot.datasets.io_utils import (
|
||||
get_parquet_file_size_in_mb,
|
||||
load_episodes,
|
||||
write_info,
|
||||
write_stats,
|
||||
write_tasks,
|
||||
)
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
|
||||
from lerobot.datasets.utils import (
|
||||
DATA_DIR,
|
||||
DEFAULT_CHUNK_SIZE,
|
||||
DEFAULT_DATA_FILE_SIZE_IN_MB,
|
||||
DEFAULT_DATA_PATH,
|
||||
DEFAULT_EPISODES_PATH,
|
||||
get_parquet_file_size_in_mb,
|
||||
load_episodes,
|
||||
update_chunk_file_indices,
|
||||
write_info,
|
||||
write_stats,
|
||||
write_tasks,
|
||||
)
|
||||
from lerobot.datasets.video_utils import encode_video_frames, get_video_info
|
||||
from lerobot.utils.constants import HF_LEROBOT_HOME, OBS_IMAGE
|
||||
@@ -918,8 +915,7 @@ def _write_parquet(df: pd.DataFrame, path: Path, meta: LeRobotDatasetMetadata) -
|
||||
|
||||
This ensures images are properly embedded and the file can be loaded correctly by HF datasets.
|
||||
"""
|
||||
from lerobot.datasets.feature_utils import get_hf_features_from_features
|
||||
from lerobot.datasets.io_utils import embed_images
|
||||
from lerobot.datasets.utils import embed_images, get_hf_features_from_features
|
||||
|
||||
hf_features = get_hf_features_from_features(meta.features)
|
||||
ep_dataset = datasets.Dataset.from_dict(df.to_dict(orient="list"), features=hf_features, split="train")
|
||||
|
||||
@@ -20,9 +20,11 @@ import torch
|
||||
|
||||
from lerobot.configs.policies import PreTrainedConfig
|
||||
from lerobot.configs.train import TrainPipelineConfig
|
||||
from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.datasets.multi_dataset import MultiLeRobotDataset
|
||||
from lerobot.datasets.lerobot_dataset import (
|
||||
LeRobotDataset,
|
||||
LeRobotDatasetMetadata,
|
||||
MultiLeRobotDataset,
|
||||
)
|
||||
from lerobot.datasets.streaming_dataset import StreamingLeRobotDataset
|
||||
from lerobot.datasets.transforms import ImageTransforms
|
||||
from lerobot.utils.constants import ACTION, OBS_PREFIX, REWARD
|
||||
|
||||
@@ -1,552 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from pprint import pformat
|
||||
from typing import Any
|
||||
|
||||
import datasets
|
||||
import numpy as np
|
||||
from PIL import Image as PILImage
|
||||
|
||||
from lerobot.configs.types import FeatureType, PolicyFeature
|
||||
from lerobot.datasets.utils import (
|
||||
DEFAULT_CHUNK_SIZE,
|
||||
DEFAULT_DATA_FILE_SIZE_IN_MB,
|
||||
DEFAULT_DATA_PATH,
|
||||
DEFAULT_FEATURES,
|
||||
DEFAULT_VIDEO_FILE_SIZE_IN_MB,
|
||||
DEFAULT_VIDEO_PATH,
|
||||
)
|
||||
from lerobot.utils.constants import ACTION, OBS_ENV_STATE, OBS_STR
|
||||
from lerobot.utils.utils import is_valid_numpy_dtype_string
|
||||
|
||||
|
||||
def get_hf_features_from_features(features: dict) -> datasets.Features:
|
||||
"""Convert a LeRobot features dictionary to a `datasets.Features` object.
|
||||
|
||||
Args:
|
||||
features (dict): A LeRobot-style feature dictionary.
|
||||
|
||||
Returns:
|
||||
datasets.Features: The corresponding Hugging Face `datasets.Features` object.
|
||||
|
||||
Raises:
|
||||
ValueError: If a feature has an unsupported shape.
|
||||
"""
|
||||
hf_features = {}
|
||||
for key, ft in features.items():
|
||||
if ft["dtype"] == "video":
|
||||
continue
|
||||
elif ft["dtype"] == "image":
|
||||
hf_features[key] = datasets.Image()
|
||||
elif ft["shape"] == (1,):
|
||||
hf_features[key] = datasets.Value(dtype=ft["dtype"])
|
||||
elif len(ft["shape"]) == 1:
|
||||
hf_features[key] = datasets.Sequence(
|
||||
length=ft["shape"][0], feature=datasets.Value(dtype=ft["dtype"])
|
||||
)
|
||||
elif len(ft["shape"]) == 2:
|
||||
hf_features[key] = datasets.Array2D(shape=ft["shape"], dtype=ft["dtype"])
|
||||
elif len(ft["shape"]) == 3:
|
||||
hf_features[key] = datasets.Array3D(shape=ft["shape"], dtype=ft["dtype"])
|
||||
elif len(ft["shape"]) == 4:
|
||||
hf_features[key] = datasets.Array4D(shape=ft["shape"], dtype=ft["dtype"])
|
||||
elif len(ft["shape"]) == 5:
|
||||
hf_features[key] = datasets.Array5D(shape=ft["shape"], dtype=ft["dtype"])
|
||||
else:
|
||||
raise ValueError(f"Corresponding feature is not valid: {ft}")
|
||||
|
||||
return datasets.Features(hf_features)
|
||||
|
||||
|
||||
def _validate_feature_names(features: dict[str, dict]) -> None:
|
||||
"""Validate that feature names do not contain invalid characters.
|
||||
|
||||
Args:
|
||||
features (dict): The LeRobot features dictionary.
|
||||
|
||||
Raises:
|
||||
ValueError: If any feature name contains '/'.
|
||||
"""
|
||||
invalid_features = {name: ft for name, ft in features.items() if "/" in name}
|
||||
if invalid_features:
|
||||
raise ValueError(f"Feature names should not contain '/'. Found '/' in '{invalid_features}'.")
|
||||
|
||||
|
||||
def hw_to_dataset_features(
|
||||
hw_features: dict[str, type | tuple], prefix: str, use_video: bool = True
|
||||
) -> dict[str, dict]:
|
||||
"""Convert hardware-specific features to a LeRobot dataset feature dictionary.
|
||||
|
||||
This function takes a dictionary describing hardware outputs (like joint states
|
||||
or camera image shapes) and formats it into the standard LeRobot feature
|
||||
specification.
|
||||
|
||||
Args:
|
||||
hw_features (dict): Dictionary mapping feature names to their type (float for
|
||||
joints) or shape (tuple for images).
|
||||
prefix (str): The prefix to add to the feature keys (e.g., "observation"
|
||||
or "action").
|
||||
use_video (bool): If True, image features are marked as "video", otherwise "image".
|
||||
|
||||
Returns:
|
||||
dict: A LeRobot features dictionary.
|
||||
"""
|
||||
features = {}
|
||||
joint_fts = {
|
||||
key: ftype
|
||||
for key, ftype in hw_features.items()
|
||||
if ftype is float or (isinstance(ftype, PolicyFeature) and ftype.type != FeatureType.VISUAL)
|
||||
}
|
||||
cam_fts = {key: shape for key, shape in hw_features.items() if isinstance(shape, tuple)}
|
||||
|
||||
if joint_fts and prefix == ACTION:
|
||||
features[prefix] = {
|
||||
"dtype": "float32",
|
||||
"shape": (len(joint_fts),),
|
||||
"names": list(joint_fts),
|
||||
}
|
||||
|
||||
if joint_fts and prefix == OBS_STR:
|
||||
features[f"{prefix}.state"] = {
|
||||
"dtype": "float32",
|
||||
"shape": (len(joint_fts),),
|
||||
"names": list(joint_fts),
|
||||
}
|
||||
|
||||
for key, shape in cam_fts.items():
|
||||
features[f"{prefix}.images.{key}"] = {
|
||||
"dtype": "video" if use_video else "image",
|
||||
"shape": shape,
|
||||
"names": ["height", "width", "channels"],
|
||||
}
|
||||
|
||||
_validate_feature_names(features)
|
||||
return features
|
||||
|
||||
|
||||
def build_dataset_frame(
|
||||
ds_features: dict[str, dict], values: dict[str, Any], prefix: str
|
||||
) -> dict[str, np.ndarray]:
|
||||
"""Construct a single data frame from raw values based on dataset features.
|
||||
|
||||
A "frame" is a dictionary containing all the data for a single timestep,
|
||||
formatted as numpy arrays according to the feature specification.
|
||||
|
||||
Args:
|
||||
ds_features (dict): The LeRobot dataset features dictionary.
|
||||
values (dict): A dictionary of raw values from the hardware/environment.
|
||||
prefix (str): The prefix to filter features by (e.g., "observation"
|
||||
or "action").
|
||||
|
||||
Returns:
|
||||
dict: A dictionary representing a single frame of data.
|
||||
"""
|
||||
frame = {}
|
||||
for key, ft in ds_features.items():
|
||||
if key in DEFAULT_FEATURES or not key.startswith(prefix):
|
||||
continue
|
||||
elif ft["dtype"] == "float32" and len(ft["shape"]) == 1:
|
||||
frame[key] = np.array([values[name] for name in ft["names"]], dtype=np.float32)
|
||||
elif ft["dtype"] in ["image", "video"]:
|
||||
frame[key] = values[key.removeprefix(f"{prefix}.images.")]
|
||||
|
||||
return frame
|
||||
|
||||
|
||||
def dataset_to_policy_features(features: dict[str, dict]) -> dict[str, PolicyFeature]:
|
||||
"""Convert dataset features to policy features.
|
||||
|
||||
This function transforms the dataset's feature specification into a format
|
||||
that a policy can use, classifying features by type (e.g., visual, state,
|
||||
action) and ensuring correct shapes (e.g., channel-first for images).
|
||||
|
||||
Args:
|
||||
features (dict): The LeRobot dataset features dictionary.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary mapping feature keys to `PolicyFeature` objects.
|
||||
|
||||
Raises:
|
||||
ValueError: If an image feature does not have a 3D shape.
|
||||
"""
|
||||
# TODO(aliberts): Implement "type" in dataset features and simplify this
|
||||
policy_features = {}
|
||||
for key, ft in features.items():
|
||||
shape = ft["shape"]
|
||||
if ft["dtype"] in ["image", "video"]:
|
||||
type = FeatureType.VISUAL
|
||||
if len(shape) != 3:
|
||||
raise ValueError(f"Number of dimensions of {key} != 3 (shape={shape})")
|
||||
|
||||
names = ft["names"]
|
||||
# Backward compatibility for "channel" which is an error introduced in LeRobotDataset v2.0 for ported datasets.
|
||||
if names[2] in ["channel", "channels"]: # (h, w, c) -> (c, h, w)
|
||||
shape = (shape[2], shape[0], shape[1])
|
||||
elif key == OBS_ENV_STATE:
|
||||
type = FeatureType.ENV
|
||||
elif key.startswith(OBS_STR):
|
||||
type = FeatureType.STATE
|
||||
elif key.startswith(ACTION):
|
||||
type = FeatureType.ACTION
|
||||
else:
|
||||
continue
|
||||
|
||||
policy_features[key] = PolicyFeature(
|
||||
type=type,
|
||||
shape=shape,
|
||||
)
|
||||
|
||||
return policy_features
|
||||
|
||||
|
||||
def combine_feature_dicts(*dicts: dict) -> dict:
|
||||
"""Merge LeRobot grouped feature dicts.
|
||||
|
||||
- For 1D numeric specs (dtype not image/video/string) with "names": we merge the names and recompute the shape.
|
||||
- For others (e.g. `observation.images.*`), the last one wins (if they are identical).
|
||||
|
||||
Args:
|
||||
*dicts: A variable number of LeRobot feature dictionaries to merge.
|
||||
|
||||
Returns:
|
||||
dict: A single merged feature dictionary.
|
||||
|
||||
Raises:
|
||||
ValueError: If there's a dtype mismatch for a feature being merged.
|
||||
"""
|
||||
out: dict = {}
|
||||
for d in dicts:
|
||||
for key, value in d.items():
|
||||
if not isinstance(value, dict):
|
||||
out[key] = value
|
||||
continue
|
||||
|
||||
dtype = value.get("dtype")
|
||||
shape = value.get("shape")
|
||||
is_vector = (
|
||||
dtype not in ("image", "video", "string")
|
||||
and isinstance(shape, tuple)
|
||||
and len(shape) == 1
|
||||
and "names" in value
|
||||
)
|
||||
|
||||
if is_vector:
|
||||
# Initialize or retrieve the accumulating dict for this feature key
|
||||
target = out.setdefault(key, {"dtype": dtype, "names": [], "shape": (0,)})
|
||||
# Ensure consistent data types across merged entries
|
||||
if "dtype" in target and dtype != target["dtype"]:
|
||||
raise ValueError(f"dtype mismatch for '{key}': {target['dtype']} vs {dtype}")
|
||||
|
||||
# Merge feature names: append only new ones to preserve order without duplicates
|
||||
seen = set(target["names"])
|
||||
for n in value["names"]:
|
||||
if n not in seen:
|
||||
target["names"].append(n)
|
||||
seen.add(n)
|
||||
# Recompute the shape to reflect the updated number of features
|
||||
target["shape"] = (len(target["names"]),)
|
||||
else:
|
||||
# For images/videos and non-1D entries: override with the latest definition
|
||||
out[key] = value
|
||||
return out
|
||||
|
||||
|
||||
def create_empty_dataset_info(
|
||||
codebase_version: str,
|
||||
fps: int,
|
||||
features: dict,
|
||||
use_videos: bool,
|
||||
robot_type: str | None = None,
|
||||
chunks_size: int | None = None,
|
||||
data_files_size_in_mb: int | None = None,
|
||||
video_files_size_in_mb: int | None = None,
|
||||
) -> dict:
|
||||
"""Create a template dictionary for a new dataset's `info.json`.
|
||||
|
||||
Args:
|
||||
codebase_version (str): The version of the LeRobot codebase.
|
||||
fps (int): The frames per second of the data.
|
||||
features (dict): The LeRobot features dictionary for the dataset.
|
||||
use_videos (bool): Whether the dataset will store videos.
|
||||
robot_type (str | None): The type of robot used, if any.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary with the initial dataset metadata.
|
||||
"""
|
||||
return {
|
||||
"codebase_version": codebase_version,
|
||||
"robot_type": robot_type,
|
||||
"total_episodes": 0,
|
||||
"total_frames": 0,
|
||||
"total_tasks": 0,
|
||||
"chunks_size": chunks_size or DEFAULT_CHUNK_SIZE,
|
||||
"data_files_size_in_mb": data_files_size_in_mb or DEFAULT_DATA_FILE_SIZE_IN_MB,
|
||||
"video_files_size_in_mb": video_files_size_in_mb or DEFAULT_VIDEO_FILE_SIZE_IN_MB,
|
||||
"fps": fps,
|
||||
"splits": {},
|
||||
"data_path": DEFAULT_DATA_PATH,
|
||||
"video_path": DEFAULT_VIDEO_PATH if use_videos else None,
|
||||
"features": features,
|
||||
}
|
||||
|
||||
|
||||
def check_delta_timestamps(
|
||||
delta_timestamps: dict[str, list[float]], fps: int, tolerance_s: float, raise_value_error: bool = True
|
||||
) -> bool:
|
||||
"""Check if delta timestamps are multiples of 1/fps +/- tolerance.
|
||||
|
||||
This ensures that adding these delta timestamps to any existing timestamp in
|
||||
the dataset will result in a value that aligns with the dataset's frame rate.
|
||||
|
||||
Args:
|
||||
delta_timestamps (dict): A dictionary where values are lists of time
|
||||
deltas in seconds.
|
||||
fps (int): The frames per second of the dataset.
|
||||
tolerance_s (float): The allowed tolerance in seconds.
|
||||
raise_value_error (bool): If True, raises an error on failure.
|
||||
|
||||
Returns:
|
||||
bool: True if all deltas are valid, False otherwise.
|
||||
|
||||
Raises:
|
||||
ValueError: If any delta is outside the tolerance and `raise_value_error` is True.
|
||||
"""
|
||||
outside_tolerance = {}
|
||||
for key, delta_ts in delta_timestamps.items():
|
||||
within_tolerance = [abs(ts * fps - round(ts * fps)) / fps <= tolerance_s for ts in delta_ts]
|
||||
if not all(within_tolerance):
|
||||
outside_tolerance[key] = [
|
||||
ts for ts, is_within in zip(delta_ts, within_tolerance, strict=True) if not is_within
|
||||
]
|
||||
|
||||
if len(outside_tolerance) > 0:
|
||||
if raise_value_error:
|
||||
raise ValueError(
|
||||
f"""
|
||||
The following delta_timestamps are found outside of tolerance range.
|
||||
Please make sure they are multiples of 1/{fps} +/- tolerance and adjust
|
||||
their values accordingly.
|
||||
\n{pformat(outside_tolerance)}
|
||||
"""
|
||||
)
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def get_delta_indices(delta_timestamps: dict[str, list[float]], fps: int) -> dict[str, list[int]]:
|
||||
"""Convert delta timestamps in seconds to delta indices in frames.
|
||||
|
||||
Args:
|
||||
delta_timestamps (dict): A dictionary of time deltas in seconds.
|
||||
fps (int): The frames per second of the dataset.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary of frame delta indices.
|
||||
"""
|
||||
delta_indices = {}
|
||||
for key, delta_ts in delta_timestamps.items():
|
||||
delta_indices[key] = [round(d * fps) for d in delta_ts]
|
||||
|
||||
return delta_indices
|
||||
|
||||
|
||||
def validate_frame(frame: dict, features: dict) -> None:
|
||||
expected_features = set(features) - set(DEFAULT_FEATURES)
|
||||
actual_features = set(frame)
|
||||
|
||||
# task is a special required field that's not part of regular features
|
||||
if "task" not in actual_features:
|
||||
raise ValueError("Feature mismatch in `frame` dictionary:\nMissing features: {'task'}\n")
|
||||
|
||||
# Remove task from actual_features for regular feature validation
|
||||
actual_features_for_validation = actual_features - {"task"}
|
||||
|
||||
error_message = validate_features_presence(actual_features_for_validation, expected_features)
|
||||
|
||||
common_features = actual_features_for_validation & expected_features
|
||||
for name in common_features:
|
||||
error_message += validate_feature_dtype_and_shape(name, features[name], frame[name])
|
||||
|
||||
if error_message:
|
||||
raise ValueError(error_message)
|
||||
|
||||
|
||||
def validate_features_presence(actual_features: set[str], expected_features: set[str]) -> str:
|
||||
"""Check for missing or extra features in a frame.
|
||||
|
||||
Args:
|
||||
actual_features (set[str]): The set of feature names present in the frame.
|
||||
expected_features (set[str]): The set of feature names expected in the frame.
|
||||
|
||||
Returns:
|
||||
str: An error message string if there's a mismatch, otherwise an empty string.
|
||||
"""
|
||||
error_message = ""
|
||||
missing_features = expected_features - actual_features
|
||||
extra_features = actual_features - expected_features
|
||||
|
||||
if missing_features or extra_features:
|
||||
error_message += "Feature mismatch in `frame` dictionary:\n"
|
||||
if missing_features:
|
||||
error_message += f"Missing features: {missing_features}\n"
|
||||
if extra_features:
|
||||
error_message += f"Extra features: {extra_features}\n"
|
||||
|
||||
return error_message
|
||||
|
||||
|
||||
def validate_feature_dtype_and_shape(
|
||||
name: str, feature: dict, value: np.ndarray | PILImage.Image | str
|
||||
) -> str:
|
||||
"""Validate the dtype and shape of a single feature's value.
|
||||
|
||||
Args:
|
||||
name (str): The name of the feature.
|
||||
feature (dict): The feature specification from the LeRobot features dictionary.
|
||||
value: The value of the feature to validate.
|
||||
|
||||
Returns:
|
||||
str: An error message if validation fails, otherwise an empty string.
|
||||
|
||||
Raises:
|
||||
NotImplementedError: If the feature dtype is not supported for validation.
|
||||
"""
|
||||
expected_dtype = feature["dtype"]
|
||||
expected_shape = feature["shape"]
|
||||
if is_valid_numpy_dtype_string(expected_dtype):
|
||||
return validate_feature_numpy_array(name, expected_dtype, expected_shape, value)
|
||||
elif expected_dtype in ["image", "video"]:
|
||||
return validate_feature_image_or_video(name, expected_shape, value)
|
||||
elif expected_dtype == "string":
|
||||
return validate_feature_string(name, value)
|
||||
else:
|
||||
raise NotImplementedError(f"The feature dtype '{expected_dtype}' is not implemented yet.")
|
||||
|
||||
|
||||
def validate_feature_numpy_array(
|
||||
name: str, expected_dtype: str, expected_shape: list[int], value: np.ndarray
|
||||
) -> str:
|
||||
"""Validate a feature that is expected to be a numpy array.
|
||||
|
||||
Args:
|
||||
name (str): The name of the feature.
|
||||
expected_dtype (str): The expected numpy dtype as a string.
|
||||
expected_shape (list[int]): The expected shape.
|
||||
value (np.ndarray): The numpy array to validate.
|
||||
|
||||
Returns:
|
||||
str: An error message if validation fails, otherwise an empty string.
|
||||
"""
|
||||
error_message = ""
|
||||
if isinstance(value, np.ndarray):
|
||||
actual_dtype = value.dtype
|
||||
actual_shape = value.shape
|
||||
|
||||
if actual_dtype != np.dtype(expected_dtype):
|
||||
error_message += f"The feature '{name}' of dtype '{actual_dtype}' is not of the expected dtype '{expected_dtype}'.\n"
|
||||
|
||||
if actual_shape != expected_shape:
|
||||
error_message += f"The feature '{name}' of shape '{actual_shape}' does not have the expected shape '{expected_shape}'.\n"
|
||||
else:
|
||||
error_message += f"The feature '{name}' is not a 'np.ndarray'. Expected type is '{expected_dtype}', but type '{type(value)}' provided instead.\n"
|
||||
|
||||
return error_message
|
||||
|
||||
|
||||
def validate_feature_image_or_video(
|
||||
name: str, expected_shape: list[str], value: np.ndarray | PILImage.Image
|
||||
) -> str:
|
||||
"""Validate a feature that is expected to be an image or video frame.
|
||||
|
||||
Accepts `np.ndarray` (channel-first or channel-last) or `PIL.Image.Image`.
|
||||
|
||||
Args:
|
||||
name (str): The name of the feature.
|
||||
expected_shape (list[str]): The expected shape (C, H, W).
|
||||
value: The image data to validate.
|
||||
|
||||
Returns:
|
||||
str: An error message if validation fails, otherwise an empty string.
|
||||
"""
|
||||
# Note: The check of pixels range ([0,1] for float and [0,255] for uint8) is done by the image writer threads.
|
||||
error_message = ""
|
||||
if isinstance(value, np.ndarray):
|
||||
actual_shape = value.shape
|
||||
c, h, w = expected_shape
|
||||
if len(actual_shape) != 3 or (actual_shape != (c, h, w) and actual_shape != (h, w, c)):
|
||||
error_message += f"The feature '{name}' of shape '{actual_shape}' does not have the expected shape '{(c, h, w)}' or '{(h, w, c)}'.\n"
|
||||
elif isinstance(value, PILImage.Image):
|
||||
pass
|
||||
else:
|
||||
error_message += f"The feature '{name}' is expected to be of type 'PIL.Image' or 'np.ndarray' channel first or channel last, but type '{type(value)}' provided instead.\n"
|
||||
|
||||
return error_message
|
||||
|
||||
|
||||
def validate_feature_string(name: str, value: str) -> str:
|
||||
"""Validate a feature that is expected to be a string.
|
||||
|
||||
Args:
|
||||
name (str): The name of the feature.
|
||||
value (str): The value to validate.
|
||||
|
||||
Returns:
|
||||
str: An error message if validation fails, otherwise an empty string.
|
||||
"""
|
||||
if not isinstance(value, str):
|
||||
return f"The feature '{name}' is expected to be of type 'str', but type '{type(value)}' provided instead.\n"
|
||||
return ""
|
||||
|
||||
|
||||
def validate_episode_buffer(episode_buffer: dict, total_episodes: int, features: dict) -> None:
|
||||
"""Validate the episode buffer before it's written to disk.
|
||||
|
||||
Ensures the buffer has the required keys, contains at least one frame, and
|
||||
has features consistent with the dataset's specification.
|
||||
|
||||
Args:
|
||||
episode_buffer (dict): The buffer containing data for a single episode.
|
||||
total_episodes (int): The current total number of episodes in the dataset.
|
||||
features (dict): The LeRobot features dictionary for the dataset.
|
||||
|
||||
Raises:
|
||||
ValueError: If the buffer is invalid.
|
||||
NotImplementedError: If the episode index is manually set and doesn't match.
|
||||
"""
|
||||
if "size" not in episode_buffer:
|
||||
raise ValueError("size key not found in episode_buffer")
|
||||
|
||||
if "task" not in episode_buffer:
|
||||
raise ValueError("task key not found in episode_buffer")
|
||||
|
||||
if episode_buffer["episode_index"] != total_episodes:
|
||||
# TODO(aliberts): Add option to use existing episode_index
|
||||
raise NotImplementedError(
|
||||
"You might have manually provided the episode_buffer with an episode_index that doesn't "
|
||||
"match the total number of episodes already in the dataset. This is not supported for now."
|
||||
)
|
||||
|
||||
if episode_buffer["size"] == 0:
|
||||
raise ValueError("You must add one or several frames with `add_frame` before calling `add_episode`.")
|
||||
|
||||
buffer_keys = set(episode_buffer.keys()) - {"task", "size"}
|
||||
if not buffer_keys == set(features):
|
||||
raise ValueError(
|
||||
f"Features from `episode_buffer` don't match the ones in `features`."
|
||||
f"In episode_buffer not in features: {buffer_keys - set(features)}"
|
||||
f"In features not in episode_buffer: {set(features) - buffer_keys}"
|
||||
)
|
||||
@@ -13,7 +13,6 @@
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import logging
|
||||
import multiprocessing
|
||||
import queue
|
||||
import threading
|
||||
@@ -23,8 +22,6 @@ import numpy as np
|
||||
import PIL.Image
|
||||
import torch
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def safe_stop_image_writer(func):
|
||||
def wrapper(*args, **kwargs):
|
||||
@@ -34,7 +31,7 @@ def safe_stop_image_writer(func):
|
||||
dataset = kwargs.get("dataset")
|
||||
image_writer = getattr(dataset, "image_writer", None) if dataset else None
|
||||
if image_writer is not None:
|
||||
logger.warning("Waiting for image writer to terminate...")
|
||||
print("Waiting for image writer to terminate...")
|
||||
image_writer.stop()
|
||||
raise e
|
||||
|
||||
@@ -92,7 +89,8 @@ def write_image(image: np.ndarray | PIL.Image.Image, fpath: Path, compress_level
|
||||
PIL.Image.Image object.
|
||||
|
||||
Side Effects:
|
||||
Logs an error message if the image writing process fails for any reason.
|
||||
Prints an error message to the console if the image writing process
|
||||
fails for any reason.
|
||||
"""
|
||||
try:
|
||||
if isinstance(image, np.ndarray):
|
||||
@@ -103,7 +101,7 @@ def write_image(image: np.ndarray | PIL.Image.Image, fpath: Path, compress_level
|
||||
raise TypeError(f"Unsupported image type: {type(image)}")
|
||||
img.save(fpath, compress_level=compress_level)
|
||||
except Exception as e:
|
||||
logger.error("Error writing image %s: %s", fpath, e)
|
||||
print(f"Error writing image {fpath}: {e}")
|
||||
|
||||
|
||||
def worker_thread_loop(queue: queue.Queue):
|
||||
|
||||
@@ -1,342 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import json
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import datasets
|
||||
import numpy as np
|
||||
import pandas
|
||||
import pandas as pd
|
||||
import pyarrow.dataset as pa_ds
|
||||
import pyarrow.parquet as pq
|
||||
import torch
|
||||
from datasets import Dataset
|
||||
from datasets.table import embed_table_storage
|
||||
from PIL import Image as PILImage
|
||||
from torchvision import transforms
|
||||
|
||||
from lerobot.datasets.utils import (
|
||||
DEFAULT_DATA_FILE_SIZE_IN_MB,
|
||||
DEFAULT_EPISODES_PATH,
|
||||
DEFAULT_SUBTASKS_PATH,
|
||||
DEFAULT_TASKS_PATH,
|
||||
EPISODES_DIR,
|
||||
INFO_PATH,
|
||||
STATS_PATH,
|
||||
flatten_dict,
|
||||
serialize_dict,
|
||||
unflatten_dict,
|
||||
)
|
||||
from lerobot.utils.utils import SuppressProgressBars
|
||||
|
||||
|
||||
def get_parquet_file_size_in_mb(parquet_path: str | Path) -> float:
|
||||
metadata = pq.read_metadata(parquet_path)
|
||||
total_uncompressed_size = 0
|
||||
for row_group in range(metadata.num_row_groups):
|
||||
rg_metadata = metadata.row_group(row_group)
|
||||
for column in range(rg_metadata.num_columns):
|
||||
col_metadata = rg_metadata.column(column)
|
||||
total_uncompressed_size += col_metadata.total_uncompressed_size
|
||||
return total_uncompressed_size / (1024**2)
|
||||
|
||||
|
||||
def get_hf_dataset_size_in_mb(hf_ds: Dataset) -> int:
|
||||
return hf_ds.data.nbytes // (1024**2)
|
||||
|
||||
|
||||
def load_nested_dataset(
|
||||
pq_dir: Path, features: datasets.Features | None = None, episodes: list[int] | None = None
|
||||
) -> Dataset:
|
||||
"""Find parquet files in provided directory {pq_dir}/chunk-xxx/file-xxx.parquet
|
||||
Convert parquet files to pyarrow memory mapped in a cache folder for efficient RAM usage
|
||||
Concatenate all pyarrow references to return HF Dataset format
|
||||
|
||||
Args:
|
||||
pq_dir: Directory containing parquet files
|
||||
features: Optional features schema to ensure consistent loading of complex types like images
|
||||
episodes: Optional list of episode indices to filter. Uses PyArrow predicate pushdown for efficiency.
|
||||
"""
|
||||
paths = sorted(pq_dir.glob("*/*.parquet"))
|
||||
if len(paths) == 0:
|
||||
raise FileNotFoundError(f"Provided directory does not contain any parquet file: {pq_dir}")
|
||||
|
||||
with SuppressProgressBars():
|
||||
# We use .from_parquet() memory-mapped loading for efficiency
|
||||
filters = pa_ds.field("episode_index").isin(episodes) if episodes is not None else None
|
||||
return Dataset.from_parquet([str(path) for path in paths], filters=filters, features=features)
|
||||
|
||||
|
||||
def get_parquet_num_frames(parquet_path: str | Path) -> int:
|
||||
metadata = pq.read_metadata(parquet_path)
|
||||
return metadata.num_rows
|
||||
|
||||
|
||||
def get_file_size_in_mb(file_path: Path) -> float:
|
||||
"""Get file size on disk in megabytes.
|
||||
|
||||
Args:
|
||||
file_path (Path): Path to the file.
|
||||
"""
|
||||
file_size_bytes = file_path.stat().st_size
|
||||
return file_size_bytes / (1024**2)
|
||||
|
||||
|
||||
def embed_images(dataset: datasets.Dataset) -> datasets.Dataset:
|
||||
"""Embed image bytes into the dataset table before saving to Parquet.
|
||||
|
||||
This function prepares a Hugging Face dataset for serialization by converting
|
||||
image objects into an embedded format that can be stored in Arrow/Parquet.
|
||||
|
||||
Args:
|
||||
dataset (datasets.Dataset): The input dataset, possibly containing image features.
|
||||
|
||||
Returns:
|
||||
datasets.Dataset: The dataset with images embedded in the table storage.
|
||||
"""
|
||||
# Embed image bytes into the table before saving to parquet
|
||||
format = dataset.format
|
||||
dataset = dataset.with_format("arrow")
|
||||
dataset = dataset.map(embed_table_storage, batched=False)
|
||||
dataset = dataset.with_format(**format)
|
||||
return dataset
|
||||
|
||||
|
||||
def load_json(fpath: Path) -> Any:
|
||||
"""Load data from a JSON file.
|
||||
|
||||
Args:
|
||||
fpath (Path): Path to the JSON file.
|
||||
|
||||
Returns:
|
||||
Any: The data loaded from the JSON file.
|
||||
"""
|
||||
with open(fpath) as f:
|
||||
return json.load(f)
|
||||
|
||||
|
||||
def write_json(data: dict, fpath: Path) -> None:
|
||||
"""Write data to a JSON file.
|
||||
|
||||
Creates parent directories if they don't exist.
|
||||
|
||||
Args:
|
||||
data (dict): The dictionary to write.
|
||||
fpath (Path): The path to the output JSON file.
|
||||
"""
|
||||
fpath.parent.mkdir(exist_ok=True, parents=True)
|
||||
with open(fpath, "w") as f:
|
||||
json.dump(data, f, indent=4, ensure_ascii=False)
|
||||
|
||||
|
||||
def write_info(info: dict, local_dir: Path) -> None:
|
||||
write_json(info, local_dir / INFO_PATH)
|
||||
|
||||
|
||||
def load_info(local_dir: Path) -> dict:
|
||||
"""Load dataset info metadata from its standard file path.
|
||||
|
||||
Also converts shape lists to tuples for consistency.
|
||||
|
||||
Args:
|
||||
local_dir (Path): The root directory of the dataset.
|
||||
|
||||
Returns:
|
||||
dict: The dataset information dictionary.
|
||||
"""
|
||||
info = load_json(local_dir / INFO_PATH)
|
||||
for ft in info["features"].values():
|
||||
ft["shape"] = tuple(ft["shape"])
|
||||
return info
|
||||
|
||||
|
||||
def write_stats(stats: dict, local_dir: Path) -> None:
|
||||
"""Serialize and write dataset statistics to their standard file path.
|
||||
|
||||
Args:
|
||||
stats (dict): The statistics dictionary (can contain tensors/numpy arrays).
|
||||
local_dir (Path): The root directory of the dataset.
|
||||
"""
|
||||
serialized_stats = serialize_dict(stats)
|
||||
write_json(serialized_stats, local_dir / STATS_PATH)
|
||||
|
||||
|
||||
def cast_stats_to_numpy(stats: dict) -> dict[str, dict[str, np.ndarray]]:
|
||||
"""Recursively cast numerical values in a stats dictionary to numpy arrays.
|
||||
|
||||
Args:
|
||||
stats (dict): The statistics dictionary.
|
||||
|
||||
Returns:
|
||||
dict: The statistics dictionary with values cast to numpy arrays.
|
||||
"""
|
||||
stats = {key: np.array(value) for key, value in flatten_dict(stats).items()}
|
||||
return unflatten_dict(stats)
|
||||
|
||||
|
||||
def load_stats(local_dir: Path) -> dict[str, dict[str, np.ndarray]] | None:
|
||||
"""Load dataset statistics and cast numerical values to numpy arrays.
|
||||
|
||||
Returns None if the stats file doesn't exist.
|
||||
|
||||
Args:
|
||||
local_dir (Path): The root directory of the dataset.
|
||||
|
||||
Returns:
|
||||
A dictionary of statistics or None if the file is not found.
|
||||
"""
|
||||
if not (local_dir / STATS_PATH).exists():
|
||||
return None
|
||||
stats = load_json(local_dir / STATS_PATH)
|
||||
return cast_stats_to_numpy(stats)
|
||||
|
||||
|
||||
def write_tasks(tasks: pandas.DataFrame, local_dir: Path) -> None:
|
||||
path = local_dir / DEFAULT_TASKS_PATH
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
tasks.to_parquet(path)
|
||||
|
||||
|
||||
def load_tasks(local_dir: Path) -> pandas.DataFrame:
|
||||
tasks = pd.read_parquet(local_dir / DEFAULT_TASKS_PATH)
|
||||
tasks.index.name = "task"
|
||||
return tasks
|
||||
|
||||
|
||||
def load_subtasks(local_dir: Path) -> pandas.DataFrame | None:
|
||||
"""Load subtasks from subtasks.parquet if it exists."""
|
||||
subtasks_path = local_dir / DEFAULT_SUBTASKS_PATH
|
||||
if subtasks_path.exists():
|
||||
return pd.read_parquet(subtasks_path)
|
||||
return None
|
||||
|
||||
|
||||
def write_episodes(episodes: Dataset, local_dir: Path) -> None:
|
||||
"""Write episode metadata to a parquet file in the LeRobot v3.0 format.
|
||||
This function writes episode-level metadata to a single parquet file.
|
||||
Used primarily during dataset conversion (v2.1 → v3.0) and in test fixtures.
|
||||
|
||||
Args:
|
||||
episodes: HuggingFace Dataset containing episode metadata
|
||||
local_dir: Root directory where the dataset will be stored
|
||||
"""
|
||||
episode_size_mb = get_hf_dataset_size_in_mb(episodes)
|
||||
if episode_size_mb > DEFAULT_DATA_FILE_SIZE_IN_MB:
|
||||
raise NotImplementedError(
|
||||
f"Episodes dataset is too large ({episode_size_mb} MB) to write to a single file. "
|
||||
f"The current limit is {DEFAULT_DATA_FILE_SIZE_IN_MB} MB. "
|
||||
"This function only supports single-file episode metadata. "
|
||||
)
|
||||
|
||||
fpath = local_dir / DEFAULT_EPISODES_PATH.format(chunk_index=0, file_index=0)
|
||||
fpath.parent.mkdir(parents=True, exist_ok=True)
|
||||
episodes.to_parquet(fpath)
|
||||
|
||||
|
||||
def load_episodes(local_dir: Path) -> datasets.Dataset:
|
||||
episodes = load_nested_dataset(local_dir / EPISODES_DIR)
|
||||
# Select episode features/columns containing references to episode data and videos
|
||||
# (e.g. tasks, dataset_from_index, dataset_to_index, data/chunk_index, data/file_index, etc.)
|
||||
# This is to speedup access to these data, instead of having to load episode stats.
|
||||
episodes = episodes.select_columns([key for key in episodes.features if not key.startswith("stats/")])
|
||||
return episodes
|
||||
|
||||
|
||||
def load_image_as_numpy(
|
||||
fpath: str | Path, dtype: np.dtype = np.float32, channel_first: bool = True
|
||||
) -> np.ndarray:
|
||||
"""Load an image from a file into a numpy array.
|
||||
|
||||
Args:
|
||||
fpath (str | Path): Path to the image file.
|
||||
dtype (np.dtype): The desired data type of the output array. If floating,
|
||||
pixels are scaled to [0, 1].
|
||||
channel_first (bool): If True, converts the image to (C, H, W) format.
|
||||
Otherwise, it remains in (H, W, C) format.
|
||||
|
||||
Returns:
|
||||
np.ndarray: The image as a numpy array.
|
||||
"""
|
||||
img = PILImage.open(fpath).convert("RGB")
|
||||
img_array = np.array(img, dtype=dtype)
|
||||
if channel_first: # (H, W, C) -> (C, H, W)
|
||||
img_array = np.transpose(img_array, (2, 0, 1))
|
||||
if np.issubdtype(dtype, np.floating):
|
||||
img_array /= 255.0
|
||||
return img_array
|
||||
|
||||
|
||||
def hf_transform_to_torch(items_dict: dict[str, list[Any]]) -> dict[str, list[torch.Tensor | str]]:
|
||||
"""Convert a batch from a Hugging Face dataset to torch tensors.
|
||||
|
||||
This transform function converts items from Hugging Face dataset format (pyarrow)
|
||||
to torch tensors. Importantly, images are converted from PIL objects (H, W, C, uint8)
|
||||
to a torch image representation (C, H, W, float32) in the range [0, 1]. Other
|
||||
types are converted to torch.tensor.
|
||||
|
||||
Args:
|
||||
items_dict (dict): A dictionary representing a batch of data from a
|
||||
Hugging Face dataset.
|
||||
|
||||
Returns:
|
||||
dict: The batch with items converted to torch tensors.
|
||||
"""
|
||||
for key in items_dict:
|
||||
first_item = items_dict[key][0]
|
||||
if isinstance(first_item, PILImage.Image):
|
||||
to_tensor = transforms.ToTensor()
|
||||
items_dict[key] = [to_tensor(img) for img in items_dict[key]]
|
||||
elif first_item is None:
|
||||
pass
|
||||
else:
|
||||
items_dict[key] = [x if isinstance(x, str) else torch.tensor(x) for x in items_dict[key]]
|
||||
return items_dict
|
||||
|
||||
|
||||
def to_parquet_with_hf_images(
|
||||
df: pandas.DataFrame, path: Path, features: datasets.Features | None = None
|
||||
) -> None:
|
||||
"""This function correctly writes to parquet a panda DataFrame that contains images encoded by HF dataset.
|
||||
This way, it can be loaded by HF dataset and correctly formatted images are returned.
|
||||
|
||||
Args:
|
||||
df: DataFrame to write to parquet.
|
||||
path: Path to write the parquet file.
|
||||
features: Optional HuggingFace Features schema. If provided, ensures image columns
|
||||
are properly typed as Image() in the parquet schema.
|
||||
"""
|
||||
# TODO(qlhoest): replace this weird synthax by `df.to_parquet(path)` only
|
||||
ds = datasets.Dataset.from_dict(df.to_dict(orient="list"), features=features)
|
||||
ds.to_parquet(path)
|
||||
|
||||
|
||||
def item_to_torch(item: dict) -> dict:
|
||||
"""Convert all items in a dictionary to PyTorch tensors where appropriate.
|
||||
|
||||
This function is used to convert an item from a streaming dataset to PyTorch tensors.
|
||||
|
||||
Args:
|
||||
item (dict): Dictionary of items from a dataset.
|
||||
|
||||
Returns:
|
||||
dict: Dictionary with all tensor-like items converted to torch.Tensor.
|
||||
"""
|
||||
for key, val in item.items():
|
||||
if isinstance(val, (np.ndarray | list)) and key not in ["task"]:
|
||||
# Convert numpy arrays and lists to torch tensors
|
||||
item[key] = torch.tensor(val)
|
||||
return item
|
||||
@@ -23,52 +23,526 @@ from pathlib import Path
|
||||
|
||||
import datasets
|
||||
import numpy as np
|
||||
import packaging.version
|
||||
import pandas as pd
|
||||
import PIL.Image
|
||||
import pyarrow as pa
|
||||
import pyarrow.parquet as pq
|
||||
import torch
|
||||
import torch.utils
|
||||
from huggingface_hub import HfApi, snapshot_download
|
||||
from huggingface_hub.errors import RevisionNotFoundError
|
||||
|
||||
from lerobot.datasets.compute_stats import compute_episode_stats
|
||||
from lerobot.datasets.dataset_metadata import CODEBASE_VERSION, LeRobotDatasetMetadata
|
||||
from lerobot.datasets.feature_utils import (
|
||||
check_delta_timestamps,
|
||||
get_delta_indices,
|
||||
get_hf_features_from_features,
|
||||
validate_episode_buffer,
|
||||
validate_frame,
|
||||
)
|
||||
from lerobot.datasets.compute_stats import aggregate_stats, compute_episode_stats
|
||||
from lerobot.datasets.image_writer import AsyncImageWriter, write_image
|
||||
from lerobot.datasets.io_utils import (
|
||||
embed_images,
|
||||
get_file_size_in_mb,
|
||||
hf_transform_to_torch,
|
||||
load_episodes,
|
||||
load_nested_dataset,
|
||||
write_info,
|
||||
)
|
||||
from lerobot.datasets.utils import (
|
||||
DEFAULT_EPISODES_PATH,
|
||||
DEFAULT_FEATURES,
|
||||
DEFAULT_IMAGE_PATH,
|
||||
INFO_PATH,
|
||||
_validate_feature_names,
|
||||
check_delta_timestamps,
|
||||
check_version_compatibility,
|
||||
create_empty_dataset_info,
|
||||
create_lerobot_dataset_card,
|
||||
embed_images,
|
||||
flatten_dict,
|
||||
get_delta_indices,
|
||||
get_file_size_in_mb,
|
||||
get_hf_features_from_features,
|
||||
get_safe_version,
|
||||
hf_transform_to_torch,
|
||||
is_valid_version,
|
||||
load_episodes,
|
||||
load_info,
|
||||
load_nested_dataset,
|
||||
load_stats,
|
||||
load_subtasks,
|
||||
load_tasks,
|
||||
update_chunk_file_indices,
|
||||
validate_episode_buffer,
|
||||
validate_frame,
|
||||
write_info,
|
||||
write_json,
|
||||
write_stats,
|
||||
write_tasks,
|
||||
)
|
||||
from lerobot.datasets.video_utils import (
|
||||
StreamingVideoEncoder,
|
||||
VideoFrame,
|
||||
concatenate_video_files,
|
||||
decode_video_frames,
|
||||
encode_video_frames,
|
||||
get_safe_default_codec,
|
||||
get_video_duration_in_s,
|
||||
get_video_info,
|
||||
resolve_vcodec,
|
||||
)
|
||||
from lerobot.utils.constants import HF_LEROBOT_HOME
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
CODEBASE_VERSION = "v3.0"
|
||||
|
||||
|
||||
class LeRobotDatasetMetadata:
|
||||
def __init__(
|
||||
self,
|
||||
repo_id: str,
|
||||
root: str | Path | None = None,
|
||||
revision: str | None = None,
|
||||
force_cache_sync: bool = False,
|
||||
metadata_buffer_size: int = 10,
|
||||
):
|
||||
self.repo_id = repo_id
|
||||
self.revision = revision if revision else CODEBASE_VERSION
|
||||
self.root = Path(root) if root is not None else HF_LEROBOT_HOME / repo_id
|
||||
self.writer = None
|
||||
self.latest_episode = None
|
||||
self.metadata_buffer: list[dict] = []
|
||||
self.metadata_buffer_size = metadata_buffer_size
|
||||
|
||||
try:
|
||||
if force_cache_sync:
|
||||
raise FileNotFoundError
|
||||
self.load_metadata()
|
||||
except (FileNotFoundError, NotADirectoryError):
|
||||
if is_valid_version(self.revision):
|
||||
self.revision = get_safe_version(self.repo_id, self.revision)
|
||||
|
||||
(self.root / "meta").mkdir(exist_ok=True, parents=True)
|
||||
self.pull_from_repo(allow_patterns="meta/")
|
||||
self.load_metadata()
|
||||
|
||||
def _flush_metadata_buffer(self) -> None:
|
||||
"""Write all buffered episode metadata to parquet file."""
|
||||
if not hasattr(self, "metadata_buffer") or len(self.metadata_buffer) == 0:
|
||||
return
|
||||
|
||||
combined_dict = {}
|
||||
for episode_dict in self.metadata_buffer:
|
||||
for key, value in episode_dict.items():
|
||||
if key not in combined_dict:
|
||||
combined_dict[key] = []
|
||||
# Extract value and serialize numpy arrays
|
||||
# because PyArrow's from_pydict function doesn't support numpy arrays
|
||||
val = value[0] if isinstance(value, list) else value
|
||||
combined_dict[key].append(val.tolist() if isinstance(val, np.ndarray) else val)
|
||||
|
||||
first_ep = self.metadata_buffer[0]
|
||||
chunk_idx = first_ep["meta/episodes/chunk_index"][0]
|
||||
file_idx = first_ep["meta/episodes/file_index"][0]
|
||||
|
||||
table = pa.Table.from_pydict(combined_dict)
|
||||
|
||||
if not self.writer:
|
||||
path = Path(self.root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx))
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
self.writer = pq.ParquetWriter(
|
||||
path, schema=table.schema, compression="snappy", use_dictionary=True
|
||||
)
|
||||
|
||||
self.writer.write_table(table)
|
||||
|
||||
self.latest_episode = self.metadata_buffer[-1]
|
||||
self.metadata_buffer.clear()
|
||||
|
||||
def _close_writer(self) -> None:
|
||||
"""Close and cleanup the parquet writer if it exists."""
|
||||
self._flush_metadata_buffer()
|
||||
|
||||
writer = getattr(self, "writer", None)
|
||||
if writer is not None:
|
||||
writer.close()
|
||||
self.writer = None
|
||||
|
||||
def __del__(self):
|
||||
"""
|
||||
Trust the user to call .finalize() but as an added safety check call the parquet writer to stop when calling the destructor
|
||||
"""
|
||||
self._close_writer()
|
||||
|
||||
def load_metadata(self):
|
||||
self.info = load_info(self.root)
|
||||
check_version_compatibility(self.repo_id, self._version, CODEBASE_VERSION)
|
||||
self.tasks = load_tasks(self.root)
|
||||
self.subtasks = load_subtasks(self.root)
|
||||
self.episodes = load_episodes(self.root)
|
||||
self.stats = load_stats(self.root)
|
||||
|
||||
def pull_from_repo(
|
||||
self,
|
||||
allow_patterns: list[str] | str | None = None,
|
||||
ignore_patterns: list[str] | str | None = None,
|
||||
) -> None:
|
||||
snapshot_download(
|
||||
self.repo_id,
|
||||
repo_type="dataset",
|
||||
revision=self.revision,
|
||||
local_dir=self.root,
|
||||
allow_patterns=allow_patterns,
|
||||
ignore_patterns=ignore_patterns,
|
||||
)
|
||||
|
||||
@property
|
||||
def url_root(self) -> str:
|
||||
return f"hf://datasets/{self.repo_id}"
|
||||
|
||||
@property
|
||||
def _version(self) -> packaging.version.Version:
|
||||
"""Codebase version used to create this dataset."""
|
||||
return packaging.version.parse(self.info["codebase_version"])
|
||||
|
||||
def get_data_file_path(self, ep_index: int) -> Path:
|
||||
if self.episodes is None:
|
||||
self.episodes = load_episodes(self.root)
|
||||
if ep_index >= len(self.episodes):
|
||||
raise IndexError(
|
||||
f"Episode index {ep_index} out of range. Episodes: {len(self.episodes) if self.episodes else 0}"
|
||||
)
|
||||
ep = self.episodes[ep_index]
|
||||
chunk_idx = ep["data/chunk_index"]
|
||||
file_idx = ep["data/file_index"]
|
||||
fpath = self.data_path.format(chunk_index=chunk_idx, file_index=file_idx)
|
||||
return Path(fpath)
|
||||
|
||||
def get_video_file_path(self, ep_index: int, vid_key: str) -> Path:
|
||||
if self.episodes is None:
|
||||
self.episodes = load_episodes(self.root)
|
||||
if ep_index >= len(self.episodes):
|
||||
raise IndexError(
|
||||
f"Episode index {ep_index} out of range. Episodes: {len(self.episodes) if self.episodes else 0}"
|
||||
)
|
||||
ep = self.episodes[ep_index]
|
||||
chunk_idx = ep[f"videos/{vid_key}/chunk_index"]
|
||||
file_idx = ep[f"videos/{vid_key}/file_index"]
|
||||
fpath = self.video_path.format(video_key=vid_key, chunk_index=chunk_idx, file_index=file_idx)
|
||||
return Path(fpath)
|
||||
|
||||
@property
|
||||
def data_path(self) -> str:
|
||||
"""Formattable string for the parquet files."""
|
||||
return self.info["data_path"]
|
||||
|
||||
@property
|
||||
def video_path(self) -> str | None:
|
||||
"""Formattable string for the video files."""
|
||||
return self.info["video_path"]
|
||||
|
||||
@property
|
||||
def robot_type(self) -> str | None:
|
||||
"""Robot type used in recording this dataset."""
|
||||
return self.info["robot_type"]
|
||||
|
||||
@property
|
||||
def fps(self) -> int:
|
||||
"""Frames per second used during data collection."""
|
||||
return self.info["fps"]
|
||||
|
||||
@property
|
||||
def features(self) -> dict[str, dict]:
|
||||
"""All features contained in the dataset."""
|
||||
return self.info["features"]
|
||||
|
||||
@property
|
||||
def image_keys(self) -> list[str]:
|
||||
"""Keys to access visual modalities stored as images."""
|
||||
return [key for key, ft in self.features.items() if ft["dtype"] == "image"]
|
||||
|
||||
@property
|
||||
def video_keys(self) -> list[str]:
|
||||
"""Keys to access visual modalities stored as videos."""
|
||||
return [key for key, ft in self.features.items() if ft["dtype"] == "video"]
|
||||
|
||||
@property
|
||||
def camera_keys(self) -> list[str]:
|
||||
"""Keys to access visual modalities (regardless of their storage method)."""
|
||||
return [key for key, ft in self.features.items() if ft["dtype"] in ["video", "image"]]
|
||||
|
||||
@property
|
||||
def names(self) -> dict[str, list | dict]:
|
||||
"""Names of the various dimensions of vector modalities."""
|
||||
return {key: ft["names"] for key, ft in self.features.items()}
|
||||
|
||||
@property
|
||||
def shapes(self) -> dict:
|
||||
"""Shapes for the different features."""
|
||||
return {key: tuple(ft["shape"]) for key, ft in self.features.items()}
|
||||
|
||||
@property
|
||||
def total_episodes(self) -> int:
|
||||
"""Total number of episodes available."""
|
||||
return self.info["total_episodes"]
|
||||
|
||||
@property
|
||||
def total_frames(self) -> int:
|
||||
"""Total number of frames saved in this dataset."""
|
||||
return self.info["total_frames"]
|
||||
|
||||
@property
|
||||
def total_tasks(self) -> int:
|
||||
"""Total number of different tasks performed in this dataset."""
|
||||
return self.info["total_tasks"]
|
||||
|
||||
@property
|
||||
def chunks_size(self) -> int:
|
||||
"""Max number of files per chunk."""
|
||||
return self.info["chunks_size"]
|
||||
|
||||
@property
|
||||
def data_files_size_in_mb(self) -> int:
|
||||
"""Max size of data file in mega bytes."""
|
||||
return self.info["data_files_size_in_mb"]
|
||||
|
||||
@property
|
||||
def video_files_size_in_mb(self) -> int:
|
||||
"""Max size of video file in mega bytes."""
|
||||
return self.info["video_files_size_in_mb"]
|
||||
|
||||
def get_task_index(self, task: str) -> int | None:
|
||||
"""
|
||||
Given a task in natural language, returns its task_index if the task already exists in the dataset,
|
||||
otherwise return None.
|
||||
"""
|
||||
if task in self.tasks.index:
|
||||
return int(self.tasks.loc[task].task_index)
|
||||
else:
|
||||
return None
|
||||
|
||||
def save_episode_tasks(self, tasks: list[str]):
|
||||
if len(set(tasks)) != len(tasks):
|
||||
raise ValueError(f"Tasks are not unique: {tasks}")
|
||||
|
||||
if self.tasks is None:
|
||||
new_tasks = tasks
|
||||
task_indices = range(len(tasks))
|
||||
self.tasks = pd.DataFrame({"task_index": task_indices}, index=pd.Index(tasks, name="task"))
|
||||
else:
|
||||
new_tasks = [task for task in tasks if task not in self.tasks.index]
|
||||
new_task_indices = range(len(self.tasks), len(self.tasks) + len(new_tasks))
|
||||
for task_idx, task in zip(new_task_indices, new_tasks, strict=False):
|
||||
self.tasks.loc[task] = task_idx
|
||||
|
||||
if len(new_tasks) > 0:
|
||||
# Update on disk
|
||||
write_tasks(self.tasks, self.root)
|
||||
|
||||
def _save_episode_metadata(self, episode_dict: dict) -> None:
|
||||
"""Buffer episode metadata and write to parquet in batches for efficiency.
|
||||
|
||||
This function accumulates episode metadata in a buffer and flushes it when the buffer
|
||||
reaches the configured size. This reduces I/O overhead by writing multiple episodes
|
||||
at once instead of one row at a time.
|
||||
|
||||
Notes: We both need to update parquet files and HF dataset:
|
||||
- `pandas` loads parquet file in RAM
|
||||
- `datasets` relies on a memory mapping from pyarrow (no RAM). It either converts parquet files to a pyarrow cache on disk,
|
||||
or loads directly from pyarrow cache.
|
||||
"""
|
||||
# Convert to list format for each value
|
||||
episode_dict = {key: [value] for key, value in episode_dict.items()}
|
||||
num_frames = episode_dict["length"][0]
|
||||
|
||||
if self.latest_episode is None:
|
||||
# Initialize indices and frame count for a new dataset made of the first episode data
|
||||
chunk_idx, file_idx = 0, 0
|
||||
if self.episodes is not None and len(self.episodes) > 0:
|
||||
# It means we are resuming recording, so we need to load the latest episode
|
||||
# Update the indices to avoid overwriting the latest episode
|
||||
chunk_idx = self.episodes[-1]["meta/episodes/chunk_index"]
|
||||
file_idx = self.episodes[-1]["meta/episodes/file_index"]
|
||||
latest_num_frames = self.episodes[-1]["dataset_to_index"]
|
||||
episode_dict["dataset_from_index"] = [latest_num_frames]
|
||||
episode_dict["dataset_to_index"] = [latest_num_frames + num_frames]
|
||||
|
||||
# When resuming, move to the next file
|
||||
chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.chunks_size)
|
||||
else:
|
||||
episode_dict["dataset_from_index"] = [0]
|
||||
episode_dict["dataset_to_index"] = [num_frames]
|
||||
|
||||
episode_dict["meta/episodes/chunk_index"] = [chunk_idx]
|
||||
episode_dict["meta/episodes/file_index"] = [file_idx]
|
||||
else:
|
||||
chunk_idx = self.latest_episode["meta/episodes/chunk_index"][0]
|
||||
file_idx = self.latest_episode["meta/episodes/file_index"][0]
|
||||
|
||||
latest_path = (
|
||||
self.root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
|
||||
if self.writer is None
|
||||
else self.writer.where
|
||||
)
|
||||
|
||||
if Path(latest_path).exists():
|
||||
latest_size_in_mb = get_file_size_in_mb(Path(latest_path))
|
||||
latest_num_frames = self.latest_episode["episode_index"][0]
|
||||
|
||||
av_size_per_frame = latest_size_in_mb / latest_num_frames if latest_num_frames > 0 else 0.0
|
||||
|
||||
if latest_size_in_mb + av_size_per_frame * num_frames >= self.data_files_size_in_mb:
|
||||
# Size limit is reached, flush buffer and prepare new parquet file
|
||||
self._flush_metadata_buffer()
|
||||
chunk_idx, file_idx = update_chunk_file_indices(chunk_idx, file_idx, self.chunks_size)
|
||||
self._close_writer()
|
||||
|
||||
# Update the existing pandas dataframe with new row
|
||||
episode_dict["meta/episodes/chunk_index"] = [chunk_idx]
|
||||
episode_dict["meta/episodes/file_index"] = [file_idx]
|
||||
episode_dict["dataset_from_index"] = [self.latest_episode["dataset_to_index"][0]]
|
||||
episode_dict["dataset_to_index"] = [self.latest_episode["dataset_to_index"][0] + num_frames]
|
||||
|
||||
# Add to buffer
|
||||
self.metadata_buffer.append(episode_dict)
|
||||
self.latest_episode = episode_dict
|
||||
|
||||
if len(self.metadata_buffer) >= self.metadata_buffer_size:
|
||||
self._flush_metadata_buffer()
|
||||
|
||||
def save_episode(
|
||||
self,
|
||||
episode_index: int,
|
||||
episode_length: int,
|
||||
episode_tasks: list[str],
|
||||
episode_stats: dict[str, dict],
|
||||
episode_metadata: dict,
|
||||
) -> None:
|
||||
episode_dict = {
|
||||
"episode_index": episode_index,
|
||||
"tasks": episode_tasks,
|
||||
"length": episode_length,
|
||||
}
|
||||
episode_dict.update(episode_metadata)
|
||||
episode_dict.update(flatten_dict({"stats": episode_stats}))
|
||||
self._save_episode_metadata(episode_dict)
|
||||
|
||||
# Update info
|
||||
self.info["total_episodes"] += 1
|
||||
self.info["total_frames"] += episode_length
|
||||
self.info["total_tasks"] = len(self.tasks)
|
||||
self.info["splits"] = {"train": f"0:{self.info['total_episodes']}"}
|
||||
|
||||
write_info(self.info, self.root)
|
||||
|
||||
self.stats = aggregate_stats([self.stats, episode_stats]) if self.stats is not None else episode_stats
|
||||
write_stats(self.stats, self.root)
|
||||
|
||||
def update_video_info(self, video_key: str | None = None) -> None:
|
||||
"""
|
||||
Warning: this function writes info from first episode videos, implicitly assuming that all videos have
|
||||
been encoded the same way. Also, this means it assumes the first episode exists.
|
||||
"""
|
||||
if video_key is not None and video_key not in self.video_keys:
|
||||
raise ValueError(f"Video key {video_key} not found in dataset")
|
||||
|
||||
video_keys = [video_key] if video_key is not None else self.video_keys
|
||||
for key in video_keys:
|
||||
if not self.features[key].get("info", None):
|
||||
video_path = self.root / self.video_path.format(video_key=key, chunk_index=0, file_index=0)
|
||||
self.info["features"][key]["info"] = get_video_info(video_path)
|
||||
|
||||
def update_chunk_settings(
|
||||
self,
|
||||
chunks_size: int | None = None,
|
||||
data_files_size_in_mb: int | None = None,
|
||||
video_files_size_in_mb: int | None = None,
|
||||
) -> None:
|
||||
"""Update chunk and file size settings after dataset creation.
|
||||
|
||||
This allows users to customize storage organization without modifying the constructor.
|
||||
These settings control how episodes are chunked and how large files can grow before
|
||||
creating new ones.
|
||||
|
||||
Args:
|
||||
chunks_size: Maximum number of files per chunk directory. If None, keeps current value.
|
||||
data_files_size_in_mb: Maximum size for data parquet files in MB. If None, keeps current value.
|
||||
video_files_size_in_mb: Maximum size for video files in MB. If None, keeps current value.
|
||||
"""
|
||||
if chunks_size is not None:
|
||||
if chunks_size <= 0:
|
||||
raise ValueError(f"chunks_size must be positive, got {chunks_size}")
|
||||
self.info["chunks_size"] = chunks_size
|
||||
|
||||
if data_files_size_in_mb is not None:
|
||||
if data_files_size_in_mb <= 0:
|
||||
raise ValueError(f"data_files_size_in_mb must be positive, got {data_files_size_in_mb}")
|
||||
self.info["data_files_size_in_mb"] = data_files_size_in_mb
|
||||
|
||||
if video_files_size_in_mb is not None:
|
||||
if video_files_size_in_mb <= 0:
|
||||
raise ValueError(f"video_files_size_in_mb must be positive, got {video_files_size_in_mb}")
|
||||
self.info["video_files_size_in_mb"] = video_files_size_in_mb
|
||||
|
||||
# Update the info file on disk
|
||||
write_info(self.info, self.root)
|
||||
|
||||
def get_chunk_settings(self) -> dict[str, int]:
|
||||
"""Get current chunk and file size settings.
|
||||
|
||||
Returns:
|
||||
Dict containing chunks_size, data_files_size_in_mb, and video_files_size_in_mb.
|
||||
"""
|
||||
return {
|
||||
"chunks_size": self.chunks_size,
|
||||
"data_files_size_in_mb": self.data_files_size_in_mb,
|
||||
"video_files_size_in_mb": self.video_files_size_in_mb,
|
||||
}
|
||||
|
||||
def __repr__(self):
|
||||
feature_keys = list(self.features)
|
||||
return (
|
||||
f"{self.__class__.__name__}({{\n"
|
||||
f" Repository ID: '{self.repo_id}',\n"
|
||||
f" Total episodes: '{self.total_episodes}',\n"
|
||||
f" Total frames: '{self.total_frames}',\n"
|
||||
f" Features: '{feature_keys}',\n"
|
||||
"})',\n"
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def create(
|
||||
cls,
|
||||
repo_id: str,
|
||||
fps: int,
|
||||
features: dict,
|
||||
robot_type: str | None = None,
|
||||
root: str | Path | None = None,
|
||||
use_videos: bool = True,
|
||||
metadata_buffer_size: int = 10,
|
||||
chunks_size: int | None = None,
|
||||
data_files_size_in_mb: int | None = None,
|
||||
video_files_size_in_mb: int | None = None,
|
||||
) -> "LeRobotDatasetMetadata":
|
||||
"""Creates metadata for a LeRobotDataset."""
|
||||
obj = cls.__new__(cls)
|
||||
obj.repo_id = repo_id
|
||||
obj.root = Path(root) if root is not None else HF_LEROBOT_HOME / repo_id
|
||||
|
||||
obj.root.mkdir(parents=True, exist_ok=False)
|
||||
|
||||
features = {**features, **DEFAULT_FEATURES}
|
||||
_validate_feature_names(features)
|
||||
|
||||
obj.tasks = None
|
||||
obj.subtasks = None
|
||||
obj.episodes = None
|
||||
obj.stats = None
|
||||
obj.info = create_empty_dataset_info(
|
||||
CODEBASE_VERSION,
|
||||
fps,
|
||||
features,
|
||||
use_videos,
|
||||
robot_type,
|
||||
chunks_size,
|
||||
data_files_size_in_mb,
|
||||
video_files_size_in_mb,
|
||||
)
|
||||
if len(obj.video_keys) > 0 and not use_videos:
|
||||
raise ValueError()
|
||||
write_json(obj.info, obj.root / INFO_PATH)
|
||||
obj.revision = None
|
||||
obj.writer = None
|
||||
obj.latest_episode = None
|
||||
obj.metadata_buffer = []
|
||||
obj.metadata_buffer_size = metadata_buffer_size
|
||||
return obj
|
||||
|
||||
|
||||
def _encode_video_worker(
|
||||
@@ -122,7 +596,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
|
||||
the dataset from that address and load it, pending your dataset is compliant with
|
||||
codebase_version v3.0. If your dataset has been created before this new format, you will be
|
||||
prompted to convert it using our conversion script from v2.1 to v3.0, which you can find at
|
||||
lerobot/scripts/convert_dataset_v21_to_v30.py.
|
||||
lerobot/datasets/v30/convert_dataset_v21_to_v30.py.
|
||||
|
||||
|
||||
2. Your dataset doesn't already exists (either on local disk or on the Hub): you can create an empty
|
||||
@@ -852,7 +1326,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
|
||||
temp_path = future.result()
|
||||
results[video_key] = temp_path
|
||||
except Exception as exc:
|
||||
logger.error(f"Video encoding failed for {video_key}: {exc}")
|
||||
logging.error(f"Video encoding failed for {video_key}: {exc}")
|
||||
raise exc
|
||||
|
||||
for video_key in self.meta.video_keys:
|
||||
@@ -891,7 +1365,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
|
||||
if end_episode is None:
|
||||
end_episode = self.num_episodes
|
||||
|
||||
logger.info(
|
||||
logging.info(
|
||||
f"Batch encoding {self.batch_encoding_size} videos for episodes {start_episode} to {end_episode - 1}"
|
||||
)
|
||||
|
||||
@@ -901,7 +1375,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
|
||||
episode_df = pd.read_parquet(episode_df_path)
|
||||
|
||||
for ep_idx in range(start_episode, end_episode):
|
||||
logger.info(f"Encoding videos for episode {ep_idx}")
|
||||
logging.info(f"Encoding videos for episode {ep_idx}")
|
||||
|
||||
if (
|
||||
self.meta.episodes[ep_idx]["data/chunk_index"] != chunk_idx
|
||||
@@ -1131,7 +1605,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
|
||||
|
||||
def start_image_writer(self, num_processes: int = 0, num_threads: int = 4) -> None:
|
||||
if isinstance(self.image_writer, AsyncImageWriter):
|
||||
logger.warning(
|
||||
logging.warning(
|
||||
"You are starting a new AsyncImageWriter that is replacing an already existing one in the dataset."
|
||||
)
|
||||
|
||||
@@ -1209,6 +1683,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
|
||||
if image_writer_processes or image_writer_threads:
|
||||
obj.start_image_writer(image_writer_processes, image_writer_threads)
|
||||
|
||||
# TODO(aliberts, rcadene, alexander-soare): Merge this with OnlineBuffer/DataBuffer
|
||||
obj.episode_buffer = obj.create_episode_buffer()
|
||||
|
||||
obj.episodes = None
|
||||
@@ -1242,3 +1717,184 @@ class LeRobotDataset(torch.utils.data.Dataset):
|
||||
obj._streaming_encoder = None
|
||||
|
||||
return obj
|
||||
|
||||
|
||||
class MultiLeRobotDataset(torch.utils.data.Dataset):
|
||||
"""A dataset consisting of multiple underlying `LeRobotDataset`s.
|
||||
|
||||
The underlying `LeRobotDataset`s are effectively concatenated, and this class adopts much of the API
|
||||
structure of `LeRobotDataset`.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
repo_ids: list[str],
|
||||
root: str | Path | None = None,
|
||||
episodes: dict | None = None,
|
||||
image_transforms: Callable | None = None,
|
||||
delta_timestamps: dict[str, list[float]] | None = None,
|
||||
tolerances_s: dict | None = None,
|
||||
download_videos: bool = True,
|
||||
video_backend: str | None = None,
|
||||
):
|
||||
super().__init__()
|
||||
self.repo_ids = repo_ids
|
||||
self.root = Path(root) if root else HF_LEROBOT_HOME
|
||||
self.tolerances_s = tolerances_s if tolerances_s else dict.fromkeys(repo_ids, 0.0001)
|
||||
# Construct the underlying datasets passing everything but `transform` and `delta_timestamps` which
|
||||
# are handled by this class.
|
||||
self._datasets = [
|
||||
LeRobotDataset(
|
||||
repo_id,
|
||||
root=self.root / repo_id,
|
||||
episodes=episodes[repo_id] if episodes else None,
|
||||
image_transforms=image_transforms,
|
||||
delta_timestamps=delta_timestamps,
|
||||
tolerance_s=self.tolerances_s[repo_id],
|
||||
download_videos=download_videos,
|
||||
video_backend=video_backend,
|
||||
)
|
||||
for repo_id in repo_ids
|
||||
]
|
||||
|
||||
# Disable any data keys that are not common across all of the datasets. Note: we may relax this
|
||||
# restriction in future iterations of this class. For now, this is necessary at least for being able
|
||||
# to use PyTorch's default DataLoader collate function.
|
||||
self.disabled_features = set()
|
||||
intersection_features = set(self._datasets[0].features)
|
||||
for ds in self._datasets:
|
||||
intersection_features.intersection_update(ds.features)
|
||||
if len(intersection_features) == 0:
|
||||
raise RuntimeError(
|
||||
"Multiple datasets were provided but they had no keys common to all of them. "
|
||||
"The multi-dataset functionality currently only keeps common keys."
|
||||
)
|
||||
for repo_id, ds in zip(self.repo_ids, self._datasets, strict=True):
|
||||
extra_keys = set(ds.features).difference(intersection_features)
|
||||
if extra_keys:
|
||||
logging.warning(
|
||||
f"keys {extra_keys} of {repo_id} were disabled as they are not contained in all the "
|
||||
"other datasets."
|
||||
)
|
||||
self.disabled_features.update(extra_keys)
|
||||
|
||||
self.image_transforms = image_transforms
|
||||
self.delta_timestamps = delta_timestamps
|
||||
# TODO(rcadene, aliberts): We should not perform this aggregation for datasets
|
||||
# with multiple robots of different ranges. Instead we should have one normalization
|
||||
# per robot.
|
||||
self.stats = aggregate_stats([dataset.meta.stats for dataset in self._datasets])
|
||||
|
||||
@property
|
||||
def repo_id_to_index(self):
|
||||
"""Return a mapping from dataset repo_id to a dataset index automatically created by this class.
|
||||
|
||||
This index is incorporated as a data key in the dictionary returned by `__getitem__`.
|
||||
"""
|
||||
return {repo_id: i for i, repo_id in enumerate(self.repo_ids)}
|
||||
|
||||
@property
|
||||
def fps(self) -> int:
|
||||
"""Frames per second used during data collection.
|
||||
|
||||
NOTE: Fow now, this relies on a check in __init__ to make sure all sub-datasets have the same info.
|
||||
"""
|
||||
return self._datasets[0].meta.info["fps"]
|
||||
|
||||
@property
|
||||
def video(self) -> bool:
|
||||
"""Returns True if this dataset loads video frames from mp4 files.
|
||||
|
||||
Returns False if it only loads images from png files.
|
||||
|
||||
NOTE: Fow now, this relies on a check in __init__ to make sure all sub-datasets have the same info.
|
||||
"""
|
||||
return self._datasets[0].meta.info.get("video", False)
|
||||
|
||||
@property
|
||||
def features(self) -> datasets.Features:
|
||||
features = {}
|
||||
for dataset in self._datasets:
|
||||
features.update({k: v for k, v in dataset.hf_features.items() if k not in self.disabled_features})
|
||||
return features
|
||||
|
||||
@property
|
||||
def camera_keys(self) -> list[str]:
|
||||
"""Keys to access image and video stream from cameras."""
|
||||
keys = []
|
||||
for key, feats in self.features.items():
|
||||
if isinstance(feats, (datasets.Image | VideoFrame)):
|
||||
keys.append(key)
|
||||
return keys
|
||||
|
||||
@property
|
||||
def video_frame_keys(self) -> list[str]:
|
||||
"""Keys to access video frames that requires to be decoded into images.
|
||||
|
||||
Note: It is empty if the dataset contains images only,
|
||||
or equal to `self.cameras` if the dataset contains videos only,
|
||||
or can even be a subset of `self.cameras` in a case of a mixed image/video dataset.
|
||||
"""
|
||||
video_frame_keys = []
|
||||
for key, feats in self.features.items():
|
||||
if isinstance(feats, VideoFrame):
|
||||
video_frame_keys.append(key)
|
||||
return video_frame_keys
|
||||
|
||||
@property
|
||||
def num_frames(self) -> int:
|
||||
"""Number of samples/frames."""
|
||||
return sum(d.num_frames for d in self._datasets)
|
||||
|
||||
@property
|
||||
def num_episodes(self) -> int:
|
||||
"""Number of episodes."""
|
||||
return sum(d.num_episodes for d in self._datasets)
|
||||
|
||||
@property
|
||||
def tolerance_s(self) -> float:
|
||||
"""Tolerance in seconds used to discard loaded frames when their timestamps
|
||||
are not close enough from the requested frames. It is only used when `delta_timestamps`
|
||||
is provided or when loading video frames from mp4 files.
|
||||
"""
|
||||
# 1e-4 to account for possible numerical error
|
||||
return 1 / self.fps - 1e-4
|
||||
|
||||
def __len__(self):
|
||||
return self.num_frames
|
||||
|
||||
def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
|
||||
if idx >= len(self):
|
||||
raise IndexError(f"Index {idx} out of bounds.")
|
||||
# Determine which dataset to get an item from based on the index.
|
||||
start_idx = 0
|
||||
dataset_idx = 0
|
||||
for dataset in self._datasets:
|
||||
if idx >= start_idx + dataset.num_frames:
|
||||
start_idx += dataset.num_frames
|
||||
dataset_idx += 1
|
||||
continue
|
||||
break
|
||||
else:
|
||||
raise AssertionError("We expect the loop to break out as long as the index is within bounds.")
|
||||
item = self._datasets[dataset_idx][idx - start_idx]
|
||||
item["dataset_index"] = torch.tensor(dataset_idx)
|
||||
for data_key in self.disabled_features:
|
||||
if data_key in item:
|
||||
del item[data_key]
|
||||
|
||||
return item
|
||||
|
||||
def __repr__(self):
|
||||
return (
|
||||
f"{self.__class__.__name__}(\n"
|
||||
f" Repository IDs: '{self.repo_ids}',\n"
|
||||
f" Number of Samples: {self.num_frames},\n"
|
||||
f" Number of Episodes: {self.num_episodes},\n"
|
||||
f" Type: {'video (.mp4)' if self.video else 'image (.png)'},\n"
|
||||
f" Recorded Frames per Second: {self.fps},\n"
|
||||
f" Camera Keys: {self.camera_keys},\n"
|
||||
f" Video Frame Keys: {self.video_frame_keys if self.video else 'N/A'},\n"
|
||||
f" Transformations: {self.image_transforms},\n"
|
||||
f")"
|
||||
)
|
||||
|
||||
@@ -1,210 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import logging
|
||||
from collections.abc import Callable
|
||||
from pathlib import Path
|
||||
|
||||
import datasets
|
||||
import torch
|
||||
import torch.utils
|
||||
|
||||
from lerobot.datasets.compute_stats import aggregate_stats
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.datasets.video_utils import VideoFrame
|
||||
from lerobot.utils.constants import HF_LEROBOT_HOME
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class MultiLeRobotDataset(torch.utils.data.Dataset):
|
||||
"""A dataset consisting of multiple underlying `LeRobotDataset`s.
|
||||
|
||||
The underlying `LeRobotDataset`s are effectively concatenated, and this class adopts much of the API
|
||||
structure of `LeRobotDataset`.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
repo_ids: list[str],
|
||||
root: str | Path | None = None,
|
||||
episodes: dict | None = None,
|
||||
image_transforms: Callable | None = None,
|
||||
delta_timestamps: dict[str, list[float]] | None = None,
|
||||
tolerances_s: dict | None = None,
|
||||
download_videos: bool = True,
|
||||
video_backend: str | None = None,
|
||||
):
|
||||
super().__init__()
|
||||
self.repo_ids = repo_ids
|
||||
self.root = Path(root) if root else HF_LEROBOT_HOME
|
||||
self.tolerances_s = tolerances_s if tolerances_s else dict.fromkeys(repo_ids, 0.0001)
|
||||
# Construct the underlying datasets passing everything but `transform` and `delta_timestamps` which
|
||||
# are handled by this class.
|
||||
self._datasets = [
|
||||
LeRobotDataset(
|
||||
repo_id,
|
||||
root=self.root / repo_id,
|
||||
episodes=episodes[repo_id] if episodes else None,
|
||||
image_transforms=image_transforms,
|
||||
delta_timestamps=delta_timestamps,
|
||||
tolerance_s=self.tolerances_s[repo_id],
|
||||
download_videos=download_videos,
|
||||
video_backend=video_backend,
|
||||
)
|
||||
for repo_id in repo_ids
|
||||
]
|
||||
|
||||
# Disable any data keys that are not common across all of the datasets. Note: we may relax this
|
||||
# restriction in future iterations of this class. For now, this is necessary at least for being able
|
||||
# to use PyTorch's default DataLoader collate function.
|
||||
self.disabled_features = set()
|
||||
intersection_features = set(self._datasets[0].features)
|
||||
for ds in self._datasets:
|
||||
intersection_features.intersection_update(ds.features)
|
||||
if len(intersection_features) == 0:
|
||||
raise RuntimeError(
|
||||
"Multiple datasets were provided but they had no keys common to all of them. "
|
||||
"The multi-dataset functionality currently only keeps common keys."
|
||||
)
|
||||
for repo_id, ds in zip(self.repo_ids, self._datasets, strict=True):
|
||||
extra_keys = set(ds.features).difference(intersection_features)
|
||||
if extra_keys:
|
||||
logger.warning(
|
||||
f"keys {extra_keys} of {repo_id} were disabled as they are not contained in all the "
|
||||
"other datasets."
|
||||
)
|
||||
self.disabled_features.update(extra_keys)
|
||||
|
||||
self.image_transforms = image_transforms
|
||||
self.delta_timestamps = delta_timestamps
|
||||
# TODO(rcadene, aliberts): We should not perform this aggregation for datasets
|
||||
# with multiple robots of different ranges. Instead we should have one normalization
|
||||
# per robot.
|
||||
self.stats = aggregate_stats([dataset.meta.stats for dataset in self._datasets])
|
||||
|
||||
@property
|
||||
def repo_id_to_index(self):
|
||||
"""Return a mapping from dataset repo_id to a dataset index automatically created by this class.
|
||||
|
||||
This index is incorporated as a data key in the dictionary returned by `__getitem__`.
|
||||
"""
|
||||
return {repo_id: i for i, repo_id in enumerate(self.repo_ids)}
|
||||
|
||||
@property
|
||||
def fps(self) -> int:
|
||||
"""Frames per second used during data collection.
|
||||
|
||||
NOTE: Fow now, this relies on a check in __init__ to make sure all sub-datasets have the same info.
|
||||
"""
|
||||
return self._datasets[0].meta.info["fps"]
|
||||
|
||||
@property
|
||||
def video(self) -> bool:
|
||||
"""Returns True if this dataset loads video frames from mp4 files.
|
||||
|
||||
Returns False if it only loads images from png files.
|
||||
|
||||
NOTE: Fow now, this relies on a check in __init__ to make sure all sub-datasets have the same info.
|
||||
"""
|
||||
return self._datasets[0].meta.info.get("video", False)
|
||||
|
||||
@property
|
||||
def features(self) -> datasets.Features:
|
||||
features = {}
|
||||
for dataset in self._datasets:
|
||||
features.update({k: v for k, v in dataset.hf_features.items() if k not in self.disabled_features})
|
||||
return features
|
||||
|
||||
@property
|
||||
def camera_keys(self) -> list[str]:
|
||||
"""Keys to access image and video stream from cameras."""
|
||||
keys = []
|
||||
for key, feats in self.features.items():
|
||||
if isinstance(feats, (datasets.Image | VideoFrame)):
|
||||
keys.append(key)
|
||||
return keys
|
||||
|
||||
@property
|
||||
def video_frame_keys(self) -> list[str]:
|
||||
"""Keys to access video frames that requires to be decoded into images.
|
||||
|
||||
Note: It is empty if the dataset contains images only,
|
||||
or equal to `self.cameras` if the dataset contains videos only,
|
||||
or can even be a subset of `self.cameras` in a case of a mixed image/video dataset.
|
||||
"""
|
||||
video_frame_keys = []
|
||||
for key, feats in self.features.items():
|
||||
if isinstance(feats, VideoFrame):
|
||||
video_frame_keys.append(key)
|
||||
return video_frame_keys
|
||||
|
||||
@property
|
||||
def num_frames(self) -> int:
|
||||
"""Number of samples/frames."""
|
||||
return sum(d.num_frames for d in self._datasets)
|
||||
|
||||
@property
|
||||
def num_episodes(self) -> int:
|
||||
"""Number of episodes."""
|
||||
return sum(d.num_episodes for d in self._datasets)
|
||||
|
||||
@property
|
||||
def tolerance_s(self) -> float:
|
||||
"""Tolerance in seconds used to discard loaded frames when their timestamps
|
||||
are not close enough from the requested frames. It is only used when `delta_timestamps`
|
||||
is provided or when loading video frames from mp4 files.
|
||||
"""
|
||||
# 1e-4 to account for possible numerical error
|
||||
return 1 / self.fps - 1e-4
|
||||
|
||||
def __len__(self):
|
||||
return self.num_frames
|
||||
|
||||
def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
|
||||
if idx >= len(self):
|
||||
raise IndexError(f"Index {idx} out of bounds.")
|
||||
# Determine which dataset to get an item from based on the index.
|
||||
start_idx = 0
|
||||
dataset_idx = 0
|
||||
for dataset in self._datasets:
|
||||
if idx >= start_idx + dataset.num_frames:
|
||||
start_idx += dataset.num_frames
|
||||
dataset_idx += 1
|
||||
continue
|
||||
break
|
||||
else:
|
||||
raise AssertionError("We expect the loop to break out as long as the index is within bounds.")
|
||||
item = self._datasets[dataset_idx][idx - start_idx]
|
||||
item["dataset_index"] = torch.tensor(dataset_idx)
|
||||
for data_key in self.disabled_features:
|
||||
if data_key in item:
|
||||
del item[data_key]
|
||||
|
||||
return item
|
||||
|
||||
def __repr__(self):
|
||||
return (
|
||||
f"{self.__class__.__name__}(\n"
|
||||
f" Repository IDs: '{self.repo_ids}',\n"
|
||||
f" Number of Samples: {self.num_frames},\n"
|
||||
f" Number of Episodes: {self.num_episodes},\n"
|
||||
f" Type: {'video (.mp4)' if self.video else 'image (.png)'},\n"
|
||||
f" Recorded Frames per Second: {self.fps},\n"
|
||||
f" Camera Keys: {self.camera_keys},\n"
|
||||
f" Video Frame Keys: {self.video_frame_keys if self.video else 'N/A'},\n"
|
||||
f" Transformations: {self.image_transforms},\n"
|
||||
f")"
|
||||
)
|
||||
382
src/lerobot/datasets/online_buffer.py
Normal file
382
src/lerobot/datasets/online_buffer.py
Normal file
@@ -0,0 +1,382 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""An online buffer for the online training loop in train.py
|
||||
|
||||
Note to maintainers: This duplicates some logic from LeRobotDataset and EpisodeAwareSampler. We should
|
||||
consider converging to one approach. Here we have opted to use numpy.memmap to back the data buffer. It's much
|
||||
faster than using HuggingFace Datasets as there's no conversion to an intermediate non-python object. Also it
|
||||
supports in-place slicing and mutation which is very handy for a dynamic buffer.
|
||||
"""
|
||||
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
|
||||
|
||||
def _make_memmap_safe(**kwargs) -> np.memmap:
|
||||
"""Make a numpy memmap with checks on available disk space first.
|
||||
|
||||
Expected kwargs are: "filename", "dtype" (must by np.dtype), "mode" and "shape"
|
||||
|
||||
For information on dtypes:
|
||||
https://numpy.org/doc/stable/reference/arrays.dtypes.html#arrays-dtypes-constructing
|
||||
"""
|
||||
if kwargs["mode"].startswith("w"):
|
||||
required_space = kwargs["dtype"].itemsize * np.prod(kwargs["shape"]) # bytes
|
||||
stats = os.statvfs(Path(kwargs["filename"]).parent)
|
||||
available_space = stats.f_bavail * stats.f_frsize # bytes
|
||||
if required_space >= available_space * 0.8:
|
||||
raise RuntimeError(
|
||||
f"You're about to take up {required_space} of {available_space} bytes available."
|
||||
)
|
||||
return np.memmap(**kwargs)
|
||||
|
||||
|
||||
class OnlineBuffer(torch.utils.data.Dataset):
|
||||
"""FIFO data buffer for the online training loop in train.py.
|
||||
|
||||
Follows the protocol of LeRobotDataset as much as is required to have it be used by the online training
|
||||
loop in the same way that a LeRobotDataset would be used.
|
||||
|
||||
The underlying data structure will have data inserted in a circular fashion. Always insert after the
|
||||
last index, and when you reach the end, wrap around to the start.
|
||||
|
||||
The data is stored in a numpy memmap.
|
||||
"""
|
||||
|
||||
NEXT_INDEX_KEY = "_next_index"
|
||||
OCCUPANCY_MASK_KEY = "_occupancy_mask"
|
||||
INDEX_KEY = "index"
|
||||
FRAME_INDEX_KEY = "frame_index"
|
||||
EPISODE_INDEX_KEY = "episode_index"
|
||||
TIMESTAMP_KEY = "timestamp"
|
||||
IS_PAD_POSTFIX = "_is_pad"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
write_dir: str | Path,
|
||||
data_spec: dict[str, Any] | None,
|
||||
buffer_capacity: int | None,
|
||||
fps: float | None = None,
|
||||
delta_timestamps: dict[str, list[float]] | dict[str, np.ndarray] | None = None,
|
||||
):
|
||||
"""
|
||||
The online buffer can be provided from scratch or you can load an existing online buffer by passing
|
||||
a `write_dir` associated with an existing buffer.
|
||||
|
||||
Args:
|
||||
write_dir: Where to keep the numpy memmap files. One memmap file will be stored for each data key.
|
||||
Note that if the files already exist, they are opened in read-write mode (used for training
|
||||
resumption.)
|
||||
data_spec: A mapping from data key to data specification, like {data_key: {"shape": tuple[int],
|
||||
"dtype": np.dtype}}. This should include all the data that you wish to record into the buffer,
|
||||
but note that "index", "frame_index" and "episode_index" are already accounted for by this
|
||||
class, so you don't need to include them.
|
||||
buffer_capacity: How many frames should be stored in the buffer as a maximum. Be aware of your
|
||||
system's available disk space when choosing this.
|
||||
fps: Same as the fps concept in LeRobot dataset. Here it needs to be provided for the
|
||||
delta_timestamps logic. You can pass None if you are not using delta_timestamps.
|
||||
delta_timestamps: Same as the delta_timestamps concept in LeRobotDataset. This is internally
|
||||
converted to dict[str, np.ndarray] for optimization purposes.
|
||||
|
||||
"""
|
||||
self.set_delta_timestamps(delta_timestamps)
|
||||
self._fps = fps
|
||||
# Tolerance in seconds used to discard loaded frames when their timestamps are not close enough from
|
||||
# the requested frames. It is only used when `delta_timestamps` is provided.
|
||||
# minus 1e-4 to account for possible numerical error
|
||||
self.tolerance_s = 1 / self.fps - 1e-4 if fps is not None else None
|
||||
self._buffer_capacity = buffer_capacity
|
||||
data_spec = self._make_data_spec(data_spec, buffer_capacity)
|
||||
Path(write_dir).mkdir(parents=True, exist_ok=True)
|
||||
self._data = {}
|
||||
for k, v in data_spec.items():
|
||||
self._data[k] = _make_memmap_safe(
|
||||
filename=Path(write_dir) / k,
|
||||
dtype=v["dtype"] if v is not None else None,
|
||||
mode="r+" if (Path(write_dir) / k).exists() else "w+",
|
||||
shape=tuple(v["shape"]) if v is not None else None,
|
||||
)
|
||||
|
||||
@property
|
||||
def delta_timestamps(self) -> dict[str, np.ndarray] | None:
|
||||
return self._delta_timestamps
|
||||
|
||||
def set_delta_timestamps(self, value: dict[str, list[float]] | None):
|
||||
"""Set delta_timestamps converting the values to numpy arrays.
|
||||
|
||||
The conversion is for an optimization in the __getitem__. The loop is much slower if the arrays
|
||||
need to be converted into numpy arrays.
|
||||
"""
|
||||
if value is not None:
|
||||
self._delta_timestamps = {k: np.array(v) for k, v in value.items()}
|
||||
else:
|
||||
self._delta_timestamps = None
|
||||
|
||||
def _make_data_spec(self, data_spec: dict[str, Any], buffer_capacity: int) -> dict[str, dict[str, Any]]:
|
||||
"""Makes the data spec for np.memmap."""
|
||||
if any(k.startswith("_") for k in data_spec):
|
||||
raise ValueError(
|
||||
"data_spec keys should not start with '_'. This prefix is reserved for internal logic."
|
||||
)
|
||||
preset_keys = {
|
||||
OnlineBuffer.INDEX_KEY,
|
||||
OnlineBuffer.FRAME_INDEX_KEY,
|
||||
OnlineBuffer.EPISODE_INDEX_KEY,
|
||||
OnlineBuffer.TIMESTAMP_KEY,
|
||||
}
|
||||
if len(intersection := set(data_spec).intersection(preset_keys)) > 0:
|
||||
raise ValueError(
|
||||
f"data_spec should not contain any of {preset_keys} as these are handled internally. "
|
||||
f"The provided data_spec has {intersection}."
|
||||
)
|
||||
complete_data_spec = {
|
||||
# _next_index will be a pointer to the next index that we should start filling from when we add
|
||||
# more data.
|
||||
OnlineBuffer.NEXT_INDEX_KEY: {"dtype": np.dtype("int64"), "shape": ()},
|
||||
# Since the memmap is initialized with all-zeros, this keeps track of which indices are occupied
|
||||
# with real data rather than the dummy initialization.
|
||||
OnlineBuffer.OCCUPANCY_MASK_KEY: {"dtype": np.dtype("?"), "shape": (buffer_capacity,)},
|
||||
OnlineBuffer.INDEX_KEY: {"dtype": np.dtype("int64"), "shape": (buffer_capacity,)},
|
||||
OnlineBuffer.FRAME_INDEX_KEY: {"dtype": np.dtype("int64"), "shape": (buffer_capacity,)},
|
||||
OnlineBuffer.EPISODE_INDEX_KEY: {"dtype": np.dtype("int64"), "shape": (buffer_capacity,)},
|
||||
OnlineBuffer.TIMESTAMP_KEY: {"dtype": np.dtype("float64"), "shape": (buffer_capacity,)},
|
||||
}
|
||||
for k, v in data_spec.items():
|
||||
complete_data_spec[k] = {"dtype": v["dtype"], "shape": (buffer_capacity, *v["shape"])}
|
||||
return complete_data_spec
|
||||
|
||||
def add_data(self, data: dict[str, np.ndarray]):
|
||||
"""Add new data to the buffer, which could potentially mean shifting old data out.
|
||||
|
||||
The new data should contain all the frames (in order) of any number of episodes. The indices should
|
||||
start from 0 (note to the developer: this can easily be generalized). See the `rollout` and
|
||||
`eval_policy` functions in `eval.py` for more information on how the data is constructed.
|
||||
|
||||
Shift the incoming data index and episode_index to continue on from the last frame. Note that this
|
||||
will be done in place!
|
||||
"""
|
||||
if len(missing_keys := (set(self.data_keys).difference(set(data)))) > 0:
|
||||
raise ValueError(f"Missing data keys: {missing_keys}")
|
||||
new_data_length = len(data[self.data_keys[0]])
|
||||
if not all(len(data[k]) == new_data_length for k in self.data_keys):
|
||||
raise ValueError("All data items should have the same length")
|
||||
|
||||
next_index = self._data[OnlineBuffer.NEXT_INDEX_KEY]
|
||||
|
||||
# Sanity check to make sure that the new data indices start from 0.
|
||||
assert data[OnlineBuffer.EPISODE_INDEX_KEY][0].item() == 0
|
||||
assert data[OnlineBuffer.INDEX_KEY][0].item() == 0
|
||||
|
||||
# Shift the incoming indices if necessary.
|
||||
if self.num_frames > 0:
|
||||
last_episode_index = self._data[OnlineBuffer.EPISODE_INDEX_KEY][next_index - 1]
|
||||
last_data_index = self._data[OnlineBuffer.INDEX_KEY][next_index - 1]
|
||||
data[OnlineBuffer.EPISODE_INDEX_KEY] += last_episode_index + 1
|
||||
data[OnlineBuffer.INDEX_KEY] += last_data_index + 1
|
||||
|
||||
# Insert the new data starting from next_index. It may be necessary to wrap around to the start.
|
||||
n_surplus = max(0, new_data_length - (self._buffer_capacity - next_index))
|
||||
for k in self.data_keys:
|
||||
if n_surplus == 0:
|
||||
slc = slice(next_index, next_index + new_data_length)
|
||||
self._data[k][slc] = data[k]
|
||||
self._data[OnlineBuffer.OCCUPANCY_MASK_KEY][slc] = True
|
||||
else:
|
||||
self._data[k][next_index:] = data[k][:-n_surplus]
|
||||
self._data[OnlineBuffer.OCCUPANCY_MASK_KEY][next_index:] = True
|
||||
self._data[k][:n_surplus] = data[k][-n_surplus:]
|
||||
if n_surplus == 0:
|
||||
self._data[OnlineBuffer.NEXT_INDEX_KEY] = next_index + new_data_length
|
||||
else:
|
||||
self._data[OnlineBuffer.NEXT_INDEX_KEY] = n_surplus
|
||||
|
||||
@property
|
||||
def data_keys(self) -> list[str]:
|
||||
keys = set(self._data)
|
||||
keys.remove(OnlineBuffer.OCCUPANCY_MASK_KEY)
|
||||
keys.remove(OnlineBuffer.NEXT_INDEX_KEY)
|
||||
return sorted(keys)
|
||||
|
||||
@property
|
||||
def fps(self) -> float | None:
|
||||
return self._fps
|
||||
|
||||
@property
|
||||
def num_episodes(self) -> int:
|
||||
return len(
|
||||
np.unique(self._data[OnlineBuffer.EPISODE_INDEX_KEY][self._data[OnlineBuffer.OCCUPANCY_MASK_KEY]])
|
||||
)
|
||||
|
||||
@property
|
||||
def num_frames(self) -> int:
|
||||
return np.count_nonzero(self._data[OnlineBuffer.OCCUPANCY_MASK_KEY])
|
||||
|
||||
def __len__(self):
|
||||
return self.num_frames
|
||||
|
||||
def _item_to_tensors(self, item: dict) -> dict:
|
||||
item_ = {}
|
||||
for k, v in item.items():
|
||||
if isinstance(v, torch.Tensor):
|
||||
item_[k] = v
|
||||
elif isinstance(v, np.ndarray):
|
||||
item_[k] = torch.from_numpy(v)
|
||||
else:
|
||||
item_[k] = torch.tensor(v)
|
||||
return item_
|
||||
|
||||
def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
|
||||
if idx >= len(self) or idx < -len(self):
|
||||
raise IndexError
|
||||
|
||||
item = {k: v[idx] for k, v in self._data.items() if not k.startswith("_")}
|
||||
|
||||
if self.delta_timestamps is None:
|
||||
return self._item_to_tensors(item)
|
||||
|
||||
episode_index = item[OnlineBuffer.EPISODE_INDEX_KEY]
|
||||
current_ts = item[OnlineBuffer.TIMESTAMP_KEY]
|
||||
episode_data_indices = np.where(
|
||||
np.bitwise_and(
|
||||
self._data[OnlineBuffer.EPISODE_INDEX_KEY] == episode_index,
|
||||
self._data[OnlineBuffer.OCCUPANCY_MASK_KEY],
|
||||
)
|
||||
)[0]
|
||||
episode_timestamps = self._data[OnlineBuffer.TIMESTAMP_KEY][episode_data_indices]
|
||||
|
||||
for data_key in self.delta_timestamps:
|
||||
# Note: The logic in this loop is copied from `load_previous_and_future_frames`.
|
||||
# Get timestamps used as query to retrieve data of previous/future frames.
|
||||
query_ts = current_ts + self.delta_timestamps[data_key]
|
||||
|
||||
# Compute distances between each query timestamp and all timestamps of all the frames belonging to
|
||||
# the episode.
|
||||
dist = np.abs(query_ts[:, None] - episode_timestamps[None, :])
|
||||
argmin_ = np.argmin(dist, axis=1)
|
||||
min_ = dist[np.arange(dist.shape[0]), argmin_]
|
||||
|
||||
is_pad = min_ > self.tolerance_s
|
||||
|
||||
# Check violated query timestamps are all outside the episode range.
|
||||
assert (
|
||||
(query_ts[is_pad] < episode_timestamps[0]) | (episode_timestamps[-1] < query_ts[is_pad])
|
||||
).all(), (
|
||||
f"One or several timestamps unexpectedly violate the tolerance ({min_} > {self.tolerance_s=}"
|
||||
") inside the episode range."
|
||||
)
|
||||
|
||||
# Load frames for this data key.
|
||||
item[data_key] = self._data[data_key][episode_data_indices[argmin_]]
|
||||
|
||||
item[f"{data_key}{OnlineBuffer.IS_PAD_POSTFIX}"] = is_pad
|
||||
|
||||
return self._item_to_tensors(item)
|
||||
|
||||
def get_data_by_key(self, key: str) -> torch.Tensor:
|
||||
"""Returns all data for a given data key as a Tensor."""
|
||||
return torch.from_numpy(self._data[key][self._data[OnlineBuffer.OCCUPANCY_MASK_KEY]])
|
||||
|
||||
|
||||
def compute_sampler_weights(
|
||||
offline_dataset: LeRobotDataset,
|
||||
offline_drop_n_last_frames: int = 0,
|
||||
online_dataset: OnlineBuffer | None = None,
|
||||
online_sampling_ratio: float | None = None,
|
||||
online_drop_n_last_frames: int = 0,
|
||||
) -> torch.Tensor:
|
||||
"""Compute the sampling weights for the online training dataloader in train.py.
|
||||
|
||||
Args:
|
||||
offline_dataset: The LeRobotDataset used for offline pre-training.
|
||||
online_drop_n_last_frames: Number of frames to drop from the end of each offline dataset episode.
|
||||
online_dataset: The OnlineBuffer used in online training.
|
||||
online_sampling_ratio: The proportion of data that should be sampled from the online dataset. If an
|
||||
online dataset is provided, this value must also be provided.
|
||||
online_drop_n_first_frames: See `offline_drop_n_last_frames`. This is the same, but for the online
|
||||
dataset.
|
||||
Returns:
|
||||
Tensor of weights for [offline_dataset; online_dataset], normalized to 1.
|
||||
|
||||
Notes to maintainers:
|
||||
- This duplicates some logic from EpisodeAwareSampler. We should consider converging to one approach.
|
||||
- When used with `torch.utils.data.WeightedRandomSampler`, it could completely replace
|
||||
`EpisodeAwareSampler` as the online dataset related arguments are optional. The only missing feature
|
||||
is the ability to turn shuffling off.
|
||||
- Options `drop_first_n_frames` and `episode_indices_to_use` can be added easily. They were not
|
||||
included here to avoid adding complexity.
|
||||
"""
|
||||
if len(offline_dataset) == 0 and (online_dataset is None or len(online_dataset) == 0):
|
||||
raise ValueError("At least one of `offline_dataset` or `online_dataset` should be contain data.")
|
||||
if (online_dataset is None) ^ (online_sampling_ratio is None):
|
||||
raise ValueError(
|
||||
"`online_dataset` and `online_sampling_ratio` must be provided together or not at all."
|
||||
)
|
||||
offline_sampling_ratio = 0 if online_sampling_ratio is None else 1 - online_sampling_ratio
|
||||
|
||||
weights = []
|
||||
|
||||
if len(offline_dataset) > 0:
|
||||
offline_data_mask_indices = []
|
||||
for start_index, end_index in zip(
|
||||
offline_dataset.meta.episodes["dataset_from_index"],
|
||||
offline_dataset.meta.episodes["dataset_to_index"],
|
||||
strict=True,
|
||||
):
|
||||
offline_data_mask_indices.extend(range(start_index, end_index - offline_drop_n_last_frames))
|
||||
offline_data_mask = torch.zeros(len(offline_dataset), dtype=torch.bool)
|
||||
offline_data_mask[torch.tensor(offline_data_mask_indices)] = True
|
||||
weights.append(
|
||||
torch.full(
|
||||
size=(len(offline_dataset),),
|
||||
fill_value=offline_sampling_ratio / offline_data_mask.sum(),
|
||||
)
|
||||
* offline_data_mask
|
||||
)
|
||||
|
||||
if online_dataset is not None and len(online_dataset) > 0:
|
||||
online_data_mask_indices = []
|
||||
episode_indices = online_dataset.get_data_by_key("episode_index")
|
||||
for episode_idx in torch.unique(episode_indices):
|
||||
where_episode = torch.where(episode_indices == episode_idx)
|
||||
start_index = where_episode[0][0]
|
||||
end_index = where_episode[0][-1] + 1
|
||||
online_data_mask_indices.extend(
|
||||
range(start_index.item(), end_index.item() - online_drop_n_last_frames)
|
||||
)
|
||||
online_data_mask = torch.zeros(len(online_dataset), dtype=torch.bool)
|
||||
online_data_mask[torch.tensor(online_data_mask_indices)] = True
|
||||
weights.append(
|
||||
torch.full(
|
||||
size=(len(online_dataset),),
|
||||
fill_value=online_sampling_ratio / online_data_mask.sum(),
|
||||
)
|
||||
* online_data_mask
|
||||
)
|
||||
|
||||
weights = torch.cat(weights)
|
||||
|
||||
if weights.sum() == 0:
|
||||
weights += 1 / len(weights)
|
||||
else:
|
||||
weights /= weights.sum()
|
||||
|
||||
return weights
|
||||
@@ -17,9 +17,8 @@ from collections.abc import Sequence
|
||||
from typing import Any
|
||||
|
||||
from lerobot.configs.types import PipelineFeatureType
|
||||
from lerobot.datasets.feature_utils import hw_to_dataset_features
|
||||
from lerobot.processor import DataProcessorPipeline
|
||||
from lerobot.types import RobotAction, RobotObservation
|
||||
from lerobot.datasets.utils import hw_to_dataset_features
|
||||
from lerobot.processor import DataProcessorPipeline, RobotAction, RobotObservation
|
||||
from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE, OBS_STR
|
||||
|
||||
|
||||
@@ -44,11 +43,11 @@ def create_initial_features(
|
||||
return features
|
||||
|
||||
|
||||
# Helper to filter state/action keys based on compiled regex patterns.
|
||||
def should_keep(key: str, patterns: tuple[re.Pattern] | None) -> bool:
|
||||
# Helper to filter state/action keys based on regex patterns.
|
||||
def should_keep(key: str, patterns: tuple[str]) -> bool:
|
||||
if patterns is None:
|
||||
return True
|
||||
return any(pat.search(key) for pat in patterns)
|
||||
return any(re.search(pat, key) for pat in patterns)
|
||||
|
||||
|
||||
def strip_prefix(key: str, prefixes_to_strip: tuple[str]) -> str:
|
||||
@@ -89,8 +88,6 @@ def aggregate_pipeline_dataset_features(
|
||||
Returns:
|
||||
A dictionary of features formatted for a Hugging Face LeRobot Dataset.
|
||||
"""
|
||||
compiled_patterns = tuple(re.compile(p) for p in patterns) if patterns is not None else None
|
||||
|
||||
all_features = pipeline.transform_features(initial_features)
|
||||
|
||||
# Intermediate storage for categorized and filtered features.
|
||||
@@ -122,7 +119,7 @@ def aggregate_pipeline_dataset_features(
|
||||
# 2. Apply filtering rules.
|
||||
if is_image and not use_videos:
|
||||
continue
|
||||
if not is_image and not should_keep(key, compiled_patterns):
|
||||
if not is_image and not should_keep(key, patterns):
|
||||
continue
|
||||
|
||||
# 3. Add the feature to the appropriate group with a clean name.
|
||||
|
||||
73
src/lerobot/datasets/push_dataset_to_hub/utils.py
Normal file
73
src/lerobot/datasets/push_dataset_to_hub/utils.py
Normal file
@@ -0,0 +1,73 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import datasets
|
||||
import torch
|
||||
|
||||
|
||||
# TODO(aliberts): remove
|
||||
def calculate_episode_data_index(hf_dataset: datasets.Dataset) -> dict[str, torch.Tensor]:
|
||||
"""
|
||||
Calculate episode data index for the provided HuggingFace Dataset. Relies on episode_index column of hf_dataset.
|
||||
|
||||
Parameters:
|
||||
- hf_dataset (datasets.Dataset): A HuggingFace dataset containing the episode index.
|
||||
|
||||
Returns:
|
||||
- episode_data_index: A dictionary containing the data index for each episode. The dictionary has two keys:
|
||||
- "from": A tensor containing the starting index of each episode.
|
||||
- "to": A tensor containing the ending index of each episode.
|
||||
"""
|
||||
episode_data_index = {"from": [], "to": []}
|
||||
|
||||
current_episode = None
|
||||
"""
|
||||
The episode_index is a list of integers, each representing the episode index of the corresponding example.
|
||||
For instance, the following is a valid episode_index:
|
||||
[0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2]
|
||||
|
||||
Below, we iterate through the episode_index and populate the episode_data_index dictionary with the starting and
|
||||
ending index of each episode. For the episode_index above, the episode_data_index dictionary will look like this:
|
||||
{
|
||||
"from": [0, 3, 7],
|
||||
"to": [3, 7, 12]
|
||||
}
|
||||
"""
|
||||
if len(hf_dataset) == 0:
|
||||
episode_data_index = {
|
||||
"from": torch.tensor([]),
|
||||
"to": torch.tensor([]),
|
||||
}
|
||||
return episode_data_index
|
||||
for idx, episode_idx in enumerate(hf_dataset["episode_index"]):
|
||||
if episode_idx != current_episode:
|
||||
# We encountered a new episode, so we append its starting location to the "from" list
|
||||
episode_data_index["from"].append(idx)
|
||||
# If this is not the first episode, we append the ending location of the previous episode to the "to" list
|
||||
if current_episode is not None:
|
||||
episode_data_index["to"].append(idx)
|
||||
# Let's keep track of the current episode index
|
||||
current_episode = episode_idx
|
||||
else:
|
||||
# We are still in the same episode, so there is nothing for us to do here
|
||||
pass
|
||||
# We have reached the end of the dataset, so we append the ending location of the last episode to the "to" list
|
||||
episode_data_index["to"].append(idx + 1)
|
||||
|
||||
for k in ["from", "to"]:
|
||||
episode_data_index[k] = torch.tensor(episode_data_index[k])
|
||||
|
||||
return episode_data_index
|
||||
@@ -13,13 +13,10 @@
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import logging
|
||||
from collections.abc import Iterator
|
||||
|
||||
import torch
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class EpisodeAwareSampler:
|
||||
def __init__(
|
||||
@@ -42,35 +39,13 @@ class EpisodeAwareSampler:
|
||||
drop_n_last_frames: Number of frames to drop from the end of each episode.
|
||||
shuffle: Whether to shuffle the indices.
|
||||
"""
|
||||
if drop_n_first_frames < 0:
|
||||
raise ValueError(f"drop_n_first_frames must be >= 0, got {drop_n_first_frames}")
|
||||
if drop_n_last_frames < 0:
|
||||
raise ValueError(f"drop_n_last_frames must be >= 0, got {drop_n_last_frames}")
|
||||
|
||||
indices = []
|
||||
for episode_idx, (start_index, end_index) in enumerate(
|
||||
zip(dataset_from_indices, dataset_to_indices, strict=True)
|
||||
):
|
||||
if episode_indices_to_use is None or episode_idx in episode_indices_to_use:
|
||||
ep_length = end_index - start_index
|
||||
if drop_n_first_frames + drop_n_last_frames >= ep_length:
|
||||
logger.warning(
|
||||
"Episode %d has %d frames but drop_n_first_frames=%d and "
|
||||
"drop_n_last_frames=%d removes all frames. Skipping.",
|
||||
episode_idx,
|
||||
ep_length,
|
||||
drop_n_first_frames,
|
||||
drop_n_last_frames,
|
||||
)
|
||||
continue
|
||||
indices.extend(range(start_index + drop_n_first_frames, end_index - drop_n_last_frames))
|
||||
|
||||
if not indices:
|
||||
raise ValueError(
|
||||
"No valid frames remain after applying drop_n_first_frames and drop_n_last_frames. "
|
||||
"All episodes were either filtered out or had too few frames."
|
||||
)
|
||||
|
||||
self.indices = indices
|
||||
self.shuffle = shuffle
|
||||
|
||||
|
||||
@@ -13,8 +13,7 @@
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from collections import deque
|
||||
from collections.abc import Callable, Generator, Iterable, Iterator
|
||||
from collections.abc import Callable, Generator, Iterator
|
||||
from pathlib import Path
|
||||
|
||||
import datasets
|
||||
@@ -22,13 +21,16 @@ import numpy as np
|
||||
import torch
|
||||
from datasets import load_dataset
|
||||
|
||||
from lerobot.datasets.dataset_metadata import CODEBASE_VERSION, LeRobotDatasetMetadata
|
||||
from lerobot.datasets.feature_utils import get_delta_indices
|
||||
from lerobot.datasets.io_utils import item_to_torch
|
||||
from lerobot.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDatasetMetadata
|
||||
from lerobot.datasets.utils import (
|
||||
Backtrackable,
|
||||
LookAheadError,
|
||||
LookBackError,
|
||||
check_version_compatibility,
|
||||
find_float_index,
|
||||
get_delta_indices,
|
||||
is_float_in_list,
|
||||
item_to_torch,
|
||||
safe_shard,
|
||||
)
|
||||
from lerobot.datasets.video_utils import (
|
||||
@@ -38,164 +40,6 @@ from lerobot.datasets.video_utils import (
|
||||
from lerobot.utils.constants import HF_LEROBOT_HOME, LOOKAHEAD_BACKTRACKTABLE, LOOKBACK_BACKTRACKTABLE
|
||||
|
||||
|
||||
class LookBackError(Exception):
|
||||
"""
|
||||
Exception raised when trying to look back in the history of a Backtrackable object.
|
||||
"""
|
||||
|
||||
pass
|
||||
|
||||
|
||||
class LookAheadError(Exception):
|
||||
"""
|
||||
Exception raised when trying to look ahead in the future of a Backtrackable object.
|
||||
"""
|
||||
|
||||
pass
|
||||
|
||||
|
||||
class Backtrackable[T]:
|
||||
"""
|
||||
Wrap any iterator/iterable so you can step back up to `history` items
|
||||
and look ahead up to `lookahead` items.
|
||||
|
||||
This is useful for streaming datasets where you need to access previous and future items
|
||||
but can't load the entire dataset into memory.
|
||||
|
||||
Example:
|
||||
-------
|
||||
```python
|
||||
ds = load_dataset("c4", "en", streaming=True, split="train")
|
||||
rev = Backtrackable(ds, history=3, lookahead=2)
|
||||
|
||||
x0 = next(rev) # forward
|
||||
x1 = next(rev)
|
||||
x2 = next(rev)
|
||||
|
||||
# Look ahead
|
||||
x3_peek = rev.peek_ahead(1) # next item without moving cursor
|
||||
x4_peek = rev.peek_ahead(2) # two items ahead
|
||||
|
||||
# Look back
|
||||
x1_again = rev.peek_back(1) # previous item without moving cursor
|
||||
x0_again = rev.peek_back(2) # two items back
|
||||
|
||||
# Move backward
|
||||
x1_back = rev.prev() # back one step
|
||||
next(rev) # returns x2, continues forward from where we were
|
||||
```
|
||||
"""
|
||||
|
||||
__slots__ = ("_source", "_back_buf", "_ahead_buf", "_cursor", "_history", "_lookahead")
|
||||
|
||||
def __init__(self, iterable: Iterable[T], *, history: int = 1, lookahead: int = 0):
|
||||
if history < 1:
|
||||
raise ValueError("history must be >= 1")
|
||||
if lookahead <= 0:
|
||||
raise ValueError("lookahead must be > 0")
|
||||
|
||||
self._source: Iterator[T] = iter(iterable)
|
||||
self._back_buf: deque[T] = deque(maxlen=history)
|
||||
self._ahead_buf: deque[T] = deque(maxlen=lookahead) if lookahead > 0 else deque()
|
||||
self._cursor: int = 0
|
||||
self._history = history
|
||||
self._lookahead = lookahead
|
||||
|
||||
def __iter__(self) -> "Backtrackable[T]":
|
||||
return self
|
||||
|
||||
def __next__(self) -> T:
|
||||
# If we've stepped back, consume from back buffer first
|
||||
if self._cursor < 0: # -1 means "last item", etc.
|
||||
self._cursor += 1
|
||||
return self._back_buf[self._cursor]
|
||||
|
||||
# If we have items in the ahead buffer, use them first
|
||||
item = self._ahead_buf.popleft() if self._ahead_buf else next(self._source)
|
||||
|
||||
# Add current item to back buffer and reset cursor
|
||||
self._back_buf.append(item)
|
||||
self._cursor = 0
|
||||
return item
|
||||
|
||||
def prev(self) -> T:
|
||||
"""
|
||||
Step one item back in history and return it.
|
||||
Raises IndexError if already at the oldest buffered item.
|
||||
"""
|
||||
if len(self._back_buf) + self._cursor <= 1:
|
||||
raise LookBackError("At start of history")
|
||||
|
||||
self._cursor -= 1
|
||||
return self._back_buf[self._cursor]
|
||||
|
||||
def peek_back(self, n: int = 1) -> T:
|
||||
"""
|
||||
Look `n` items back (n=1 == previous item) without moving the cursor.
|
||||
"""
|
||||
if n < 0 or n + 1 > len(self._back_buf) + self._cursor:
|
||||
raise LookBackError("peek_back distance out of range")
|
||||
|
||||
return self._back_buf[self._cursor - (n + 1)]
|
||||
|
||||
def peek_ahead(self, n: int = 1) -> T:
|
||||
"""
|
||||
Look `n` items ahead (n=1 == next item) without moving the cursor.
|
||||
Fills the ahead buffer if necessary.
|
||||
"""
|
||||
if n < 1:
|
||||
raise LookAheadError("peek_ahead distance must be 1 or more")
|
||||
elif n > self._lookahead:
|
||||
raise LookAheadError("peek_ahead distance exceeds lookahead limit")
|
||||
|
||||
# Fill ahead buffer if we don't have enough items
|
||||
while len(self._ahead_buf) < n:
|
||||
try:
|
||||
item = next(self._source)
|
||||
self._ahead_buf.append(item)
|
||||
|
||||
except StopIteration as err:
|
||||
raise LookAheadError("peek_ahead: not enough items in source") from err
|
||||
|
||||
return self._ahead_buf[n - 1]
|
||||
|
||||
def history(self) -> list[T]:
|
||||
"""
|
||||
Return a copy of the buffered history (most recent last).
|
||||
The list length ≤ `history` argument passed at construction.
|
||||
"""
|
||||
if self._cursor == 0:
|
||||
return list(self._back_buf)
|
||||
|
||||
# When cursor<0, slice so the order remains chronological
|
||||
return list(self._back_buf)[: self._cursor or None]
|
||||
|
||||
def can_peek_back(self, steps: int = 1) -> bool:
|
||||
"""
|
||||
Check if we can go back `steps` items without raising an IndexError.
|
||||
"""
|
||||
return steps <= len(self._back_buf) + self._cursor
|
||||
|
||||
def can_peek_ahead(self, steps: int = 1) -> bool:
|
||||
"""
|
||||
Check if we can peek ahead `steps` items.
|
||||
This may involve trying to fill the ahead buffer.
|
||||
"""
|
||||
if self._lookahead > 0 and steps > self._lookahead:
|
||||
return False
|
||||
|
||||
# Try to fill ahead buffer to check if we can peek that far
|
||||
try:
|
||||
while len(self._ahead_buf) < steps:
|
||||
if self._lookahead > 0 and len(self._ahead_buf) >= self._lookahead:
|
||||
return False
|
||||
item = next(self._source)
|
||||
self._ahead_buf.append(item)
|
||||
return True
|
||||
except StopIteration:
|
||||
return False
|
||||
|
||||
|
||||
class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
|
||||
"""LeRobotDataset with streaming capabilities.
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -28,7 +28,7 @@ quantile statistics (q01, q10, q50, q90, q99) in their metadata. This script:
|
||||
Usage:
|
||||
|
||||
```bash
|
||||
python src/lerobot/scripts/augment_dataset_quantile_stats.py \
|
||||
python src/lerobot/datasets/v30/augment_dataset_quantile_stats.py \
|
||||
--repo-id=lerobot/pusht \
|
||||
```
|
||||
"""
|
||||
@@ -45,9 +45,8 @@ from requests import HTTPError
|
||||
from tqdm import tqdm
|
||||
|
||||
from lerobot.datasets.compute_stats import DEFAULT_QUANTILES, aggregate_stats, get_feature_stats
|
||||
from lerobot.datasets.dataset_metadata import CODEBASE_VERSION
|
||||
from lerobot.datasets.io_utils import write_stats
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
|
||||
from lerobot.datasets.utils import write_stats
|
||||
from lerobot.utils.utils import init_logging
|
||||
|
||||
|
||||
@@ -28,13 +28,13 @@ Usage:
|
||||
|
||||
Convert a dataset from the hub:
|
||||
```bash
|
||||
python src/lerobot/scripts/convert_dataset_v21_to_v30.py \
|
||||
python src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py \
|
||||
--repo-id=lerobot/pusht
|
||||
```
|
||||
|
||||
Convert a local dataset (works in place):
|
||||
```bash
|
||||
python src/lerobot/scripts/convert_dataset_v21_to_v30.py \
|
||||
python src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py \
|
||||
--repo-id=lerobot/pusht \
|
||||
--root=/path/to/local/dataset/directory \
|
||||
--push-to-hub=false
|
||||
@@ -60,19 +60,7 @@ from huggingface_hub import HfApi, snapshot_download
|
||||
from requests import HTTPError
|
||||
|
||||
from lerobot.datasets.compute_stats import aggregate_stats
|
||||
from lerobot.datasets.dataset_metadata import CODEBASE_VERSION
|
||||
from lerobot.datasets.io_utils import (
|
||||
cast_stats_to_numpy,
|
||||
get_file_size_in_mb,
|
||||
get_parquet_file_size_in_mb,
|
||||
get_parquet_num_frames,
|
||||
load_info,
|
||||
write_episodes,
|
||||
write_info,
|
||||
write_stats,
|
||||
write_tasks,
|
||||
)
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
|
||||
from lerobot.datasets.utils import (
|
||||
DEFAULT_CHUNK_SIZE,
|
||||
DEFAULT_DATA_FILE_SIZE_IN_MB,
|
||||
@@ -82,8 +70,17 @@ from lerobot.datasets.utils import (
|
||||
LEGACY_EPISODES_PATH,
|
||||
LEGACY_EPISODES_STATS_PATH,
|
||||
LEGACY_TASKS_PATH,
|
||||
cast_stats_to_numpy,
|
||||
flatten_dict,
|
||||
get_file_size_in_mb,
|
||||
get_parquet_file_size_in_mb,
|
||||
get_parquet_num_frames,
|
||||
load_info,
|
||||
update_chunk_file_indices,
|
||||
write_episodes,
|
||||
write_info,
|
||||
write_stats,
|
||||
write_tasks,
|
||||
)
|
||||
from lerobot.datasets.video_utils import concatenate_video_files, get_video_duration_in_s
|
||||
from lerobot.utils.constants import HF_LEROBOT_HOME
|
||||
@@ -231,6 +228,7 @@ def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int):
|
||||
|
||||
# Reset for the next file
|
||||
size_in_mb = 0
|
||||
num_frames += ep_num_frames # Still need to accumulate total frames
|
||||
paths_to_cat = []
|
||||
|
||||
# Now create metadata with correct chunk/file indices
|
||||
@@ -37,8 +37,6 @@ import torchvision
|
||||
from datasets.features.features import register_feature
|
||||
from PIL import Image
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# List of hardware encoders to probe for auto-selection. Availability depends on the platform and FFmpeg build.
|
||||
# Determines the order of preference for auto-selection when vcodec="auto" is used.
|
||||
HW_ENCODERS = [
|
||||
@@ -96,7 +94,7 @@ def detect_available_hw_encoders() -> list[str]:
|
||||
av.codec.Codec(codec_name, "w")
|
||||
available.append(codec_name)
|
||||
except Exception: # nosec B110
|
||||
logger.debug("HW encoder '%s' not available", codec_name) # nosec B110
|
||||
pass # nosec B110
|
||||
return available
|
||||
|
||||
|
||||
@@ -105,14 +103,14 @@ def resolve_vcodec(vcodec: str) -> str:
|
||||
if vcodec not in VALID_VIDEO_CODECS:
|
||||
raise ValueError(f"Invalid vcodec '{vcodec}'. Must be one of: {sorted(VALID_VIDEO_CODECS)}")
|
||||
if vcodec != "auto":
|
||||
logger.info(f"Using video codec: {vcodec}")
|
||||
logging.info(f"Using video codec: {vcodec}")
|
||||
return vcodec
|
||||
available = detect_available_hw_encoders()
|
||||
for encoder in HW_ENCODERS:
|
||||
if encoder in available:
|
||||
logger.info(f"Auto-selected video codec: {encoder}")
|
||||
logging.info(f"Auto-selected video codec: {encoder}")
|
||||
return encoder
|
||||
logger.info("No hardware encoder available, falling back to software encoder 'libsvtav1'")
|
||||
logging.info("No hardware encoder available, falling back to software encoder 'libsvtav1'")
|
||||
return "libsvtav1"
|
||||
|
||||
|
||||
@@ -120,7 +118,7 @@ def get_safe_default_codec():
|
||||
if importlib.util.find_spec("torchcodec"):
|
||||
return "torchcodec"
|
||||
else:
|
||||
logger.warning(
|
||||
logging.warning(
|
||||
"'torchcodec' is not available in your platform, falling back to 'pyav' as a default decoder"
|
||||
)
|
||||
return "pyav"
|
||||
@@ -210,7 +208,7 @@ def decode_video_frames_torchvision(
|
||||
for frame in reader:
|
||||
current_ts = frame["pts"]
|
||||
if log_loaded_timestamps:
|
||||
logger.info(f"frame loaded at timestamp={current_ts:.4f}")
|
||||
logging.info(f"frame loaded at timestamp={current_ts:.4f}")
|
||||
loaded_frames.append(frame["data"])
|
||||
loaded_ts.append(current_ts)
|
||||
if current_ts >= last_ts:
|
||||
@@ -246,7 +244,7 @@ def decode_video_frames_torchvision(
|
||||
closest_ts = loaded_ts[argmin_]
|
||||
|
||||
if log_loaded_timestamps:
|
||||
logger.info(f"{closest_ts=}")
|
||||
logging.info(f"{closest_ts=}")
|
||||
|
||||
# convert to the pytorch format which is float32 in [0,1] range (and channel first)
|
||||
closest_frames = closest_frames.type(torch.float32) / 255
|
||||
@@ -350,7 +348,7 @@ def decode_video_frames_torchcodec(
|
||||
loaded_frames.append(frame)
|
||||
loaded_ts.append(pts.item())
|
||||
if log_loaded_timestamps:
|
||||
logger.info(f"Frame loaded at timestamp={pts:.4f}")
|
||||
logging.info(f"Frame loaded at timestamp={pts:.4f}")
|
||||
|
||||
query_ts = torch.tensor(timestamps)
|
||||
loaded_ts = torch.tensor(loaded_ts)
|
||||
@@ -376,7 +374,7 @@ def decode_video_frames_torchcodec(
|
||||
closest_ts = loaded_ts[argmin_]
|
||||
|
||||
if log_loaded_timestamps:
|
||||
logger.info(f"{closest_ts=}")
|
||||
logging.info(f"{closest_ts=}")
|
||||
|
||||
# convert to float32 in [0,1] range
|
||||
closest_frames = (closest_frames / 255.0).type(torch.float32)
|
||||
@@ -410,14 +408,14 @@ def encode_video_frames(
|
||||
imgs_dir = Path(imgs_dir)
|
||||
|
||||
if video_path.exists() and not overwrite:
|
||||
logger.warning(f"Video file already exists: {video_path}. Skipping encoding.")
|
||||
logging.warning(f"Video file already exists: {video_path}. Skipping encoding.")
|
||||
return
|
||||
|
||||
video_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Encoders/pixel formats incompatibility check
|
||||
if (vcodec == "libsvtav1" or vcodec == "hevc") and pix_fmt == "yuv444p":
|
||||
logger.warning(
|
||||
logging.warning(
|
||||
f"Incompatible pixel format 'yuv444p' for codec {vcodec}, auto-selecting format 'yuv420p'"
|
||||
)
|
||||
pix_fmt = "yuv420p"
|
||||
@@ -510,7 +508,7 @@ def concatenate_video_files(
|
||||
output_video_path = Path(output_video_path)
|
||||
|
||||
if output_video_path.exists() and not overwrite:
|
||||
logger.warning(f"Video file already exists: {output_video_path}. Skipping concatenation.")
|
||||
logging.warning(f"Video file already exists: {output_video_path}. Skipping concatenation.")
|
||||
return
|
||||
|
||||
output_video_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
@@ -695,7 +693,7 @@ class _CameraEncoderThread(threading.Thread):
|
||||
self.result_queue.put(("ok", None))
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Encoder thread error: {e}")
|
||||
logging.error(f"Encoder thread error: {e}")
|
||||
if container is not None:
|
||||
with contextlib.suppress(Exception):
|
||||
container.close()
|
||||
@@ -821,7 +819,7 @@ class StreamingVideoEncoder:
|
||||
count = self._dropped_frames[video_key]
|
||||
# Log periodically to avoid spam (1st, then every 10th)
|
||||
if count == 1 or count % 10 == 0:
|
||||
logger.warning(
|
||||
logging.warning(
|
||||
f"Encoder queue full for {video_key}, dropped {count} frame(s). "
|
||||
f"Consider using vcodec='auto' for hardware encoding or increasing encoder_queue_maxsize."
|
||||
)
|
||||
@@ -843,7 +841,7 @@ class StreamingVideoEncoder:
|
||||
# Report dropped frames
|
||||
for video_key, count in self._dropped_frames.items():
|
||||
if count > 0:
|
||||
logger.warning(f"Episode finished with {count} dropped frame(s) for {video_key}.")
|
||||
logging.warning(f"Episode finished with {count} dropped frame(s) for {video_key}.")
|
||||
|
||||
# Send sentinel to all queues
|
||||
for video_key in self._frame_queues:
|
||||
@@ -853,7 +851,7 @@ class StreamingVideoEncoder:
|
||||
for video_key in self._threads:
|
||||
self._threads[video_key].join(timeout=120)
|
||||
if self._threads[video_key].is_alive():
|
||||
logger.error(f"Encoder thread for {video_key} did not finish in time")
|
||||
logging.error(f"Encoder thread for {video_key} did not finish in time")
|
||||
self._stop_events[video_key].set()
|
||||
self._threads[video_key].join(timeout=5)
|
||||
results[video_key] = (self._video_paths[video_key], None)
|
||||
@@ -865,7 +863,7 @@ class StreamingVideoEncoder:
|
||||
raise RuntimeError(f"Encoder thread for {video_key} failed: {data}")
|
||||
results[video_key] = (self._video_paths[video_key], data)
|
||||
except queue.Empty:
|
||||
logger.error(f"No result from encoder thread for {video_key}")
|
||||
logging.error(f"No result from encoder thread for {video_key}")
|
||||
results[video_key] = (self._video_paths[video_key], None)
|
||||
|
||||
self._cleanup()
|
||||
@@ -1073,13 +1071,13 @@ class VideoEncodingManager:
|
||||
elif self.dataset.episodes_since_last_encoding > 0:
|
||||
# Handle any remaining episodes that haven't been batch encoded
|
||||
if exc_type is not None:
|
||||
logger.info("Exception occurred. Encoding remaining episodes before exit...")
|
||||
logging.info("Exception occurred. Encoding remaining episodes before exit...")
|
||||
else:
|
||||
logger.info("Recording stopped. Encoding remaining episodes...")
|
||||
logging.info("Recording stopped. Encoding remaining episodes...")
|
||||
|
||||
start_ep = self.dataset.num_episodes - self.dataset.episodes_since_last_encoding
|
||||
end_ep = self.dataset.num_episodes
|
||||
logger.info(
|
||||
logging.info(
|
||||
f"Encoding remaining {self.dataset.episodes_since_last_encoding} episodes, "
|
||||
f"from episode {start_ep} to {end_ep - 1}"
|
||||
)
|
||||
@@ -1096,7 +1094,7 @@ class VideoEncodingManager:
|
||||
episode_index=interrupted_episode_index, image_key=key, frame_index=0
|
||||
).parent
|
||||
if img_dir.exists():
|
||||
logger.debug(
|
||||
logging.debug(
|
||||
f"Cleaning up interrupted episode images for episode {interrupted_episode_index}, camera {key}"
|
||||
)
|
||||
shutil.rmtree(img_dir)
|
||||
@@ -1107,8 +1105,8 @@ class VideoEncodingManager:
|
||||
png_files = list(img_dir.rglob("*.png"))
|
||||
if len(png_files) == 0:
|
||||
shutil.rmtree(img_dir)
|
||||
logger.debug("Cleaned up empty images directory")
|
||||
logging.debug("Cleaned up empty images directory")
|
||||
else:
|
||||
logger.debug(f"Images directory is not empty, containing {len(png_files)} PNG files")
|
||||
logging.debug(f"Images directory is not empty, containing {len(png_files)} PNG files")
|
||||
|
||||
return False # Don't suppress the original exception
|
||||
|
||||
@@ -29,7 +29,7 @@ from gymnasium import spaces
|
||||
from libero.libero import benchmark, get_libero_path
|
||||
from libero.libero.envs import OffScreenRenderEnv
|
||||
|
||||
from lerobot.types import RobotObservation
|
||||
from lerobot.processor import RobotObservation
|
||||
|
||||
|
||||
def _parse_camera_names(camera_name: str | Sequence[str]) -> list[str]:
|
||||
|
||||
@@ -25,7 +25,7 @@ import metaworld.policies as policies
|
||||
import numpy as np
|
||||
from gymnasium import spaces
|
||||
|
||||
from lerobot.types import RobotObservation
|
||||
from lerobot.processor import RobotObservation
|
||||
|
||||
# ---- Load configuration data from the external JSON file ----
|
||||
CONFIG_PATH = Path(__file__).parent / "metaworld_config.json"
|
||||
|
||||
@@ -29,7 +29,7 @@ from torch import Tensor
|
||||
|
||||
from lerobot.configs.types import FeatureType, PolicyFeature
|
||||
from lerobot.envs.configs import EnvConfig
|
||||
from lerobot.types import RobotObservation
|
||||
from lerobot.processor import RobotObservation
|
||||
from lerobot.utils.constants import OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE, OBS_STR
|
||||
from lerobot.utils.utils import get_channel_first_image_shape
|
||||
|
||||
|
||||
@@ -29,7 +29,7 @@ from dataclasses import dataclass
|
||||
from enum import Enum
|
||||
from functools import cached_property
|
||||
from pprint import pformat
|
||||
from typing import Protocol
|
||||
from typing import Protocol, TypeAlias
|
||||
|
||||
import serial
|
||||
from deepdiff import DeepDiff
|
||||
@@ -38,8 +38,8 @@ from tqdm import tqdm
|
||||
from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
|
||||
from lerobot.utils.utils import enter_pressed, move_cursor_up
|
||||
|
||||
type NameOrID = str | int
|
||||
type Value = int | float
|
||||
NameOrID: TypeAlias = str | int
|
||||
Value: TypeAlias = int | float
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -1277,4 +1277,4 @@ class SerialMotorsBus(MotorsBusBase):
|
||||
|
||||
|
||||
# Backward compatibility alias
|
||||
MotorsBus = SerialMotorsBus
|
||||
MotorsBus: TypeAlias = SerialMotorsBus
|
||||
|
||||
@@ -23,8 +23,7 @@ import draccus
|
||||
import torch
|
||||
from safetensors.torch import load_file, save_file
|
||||
|
||||
from lerobot.datasets.io_utils import write_json
|
||||
from lerobot.datasets.utils import flatten_dict, unflatten_dict
|
||||
from lerobot.datasets.utils import flatten_dict, unflatten_dict, write_json
|
||||
from lerobot.utils.constants import (
|
||||
OPTIMIZER_PARAM_GROUPS,
|
||||
OPTIMIZER_STATE,
|
||||
|
||||
@@ -23,7 +23,7 @@ import draccus
|
||||
from torch.optim import Optimizer
|
||||
from torch.optim.lr_scheduler import LambdaLR, LRScheduler
|
||||
|
||||
from lerobot.datasets.io_utils import write_json
|
||||
from lerobot.datasets.utils import write_json
|
||||
from lerobot.utils.constants import SCHEDULER_STATE
|
||||
from lerobot.utils.io_utils import deserialize_json_into_object
|
||||
|
||||
|
||||
@@ -18,14 +18,15 @@ from __future__ import annotations
|
||||
|
||||
import importlib
|
||||
import logging
|
||||
from typing import Any, TypedDict, Unpack
|
||||
from typing import Any, TypedDict
|
||||
|
||||
import torch
|
||||
from typing_extensions import Unpack
|
||||
|
||||
from lerobot.configs.policies import PreTrainedConfig
|
||||
from lerobot.configs.types import FeatureType
|
||||
from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
|
||||
from lerobot.datasets.feature_utils import dataset_to_policy_features
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata
|
||||
from lerobot.datasets.utils import dataset_to_policy_features
|
||||
from lerobot.envs.configs import EnvConfig
|
||||
from lerobot.envs.utils import env_to_policy_features
|
||||
from lerobot.policies.act.configuration_act import ACTConfig
|
||||
@@ -43,14 +44,13 @@ from lerobot.policies.utils import validate_visual_features_consistency
|
||||
from lerobot.policies.vqbet.configuration_vqbet import VQBeTConfig
|
||||
from lerobot.policies.wall_x.configuration_wall_x import WallXConfig
|
||||
from lerobot.policies.xvla.configuration_xvla import XVLAConfig
|
||||
from lerobot.processor import PolicyProcessorPipeline
|
||||
from lerobot.processor import PolicyAction, PolicyProcessorPipeline
|
||||
from lerobot.processor.converters import (
|
||||
batch_to_transition,
|
||||
policy_action_to_transition,
|
||||
transition_to_batch,
|
||||
transition_to_policy_action,
|
||||
)
|
||||
from lerobot.types import PolicyAction
|
||||
from lerobot.utils.constants import (
|
||||
ACTION,
|
||||
POLICY_POSTPROCESSOR_DEFAULT_NAME,
|
||||
|
||||
@@ -4,9 +4,10 @@
|
||||
# Licensed under The MIT License [see LICENSE for details]
|
||||
# --------------------------------------------------------
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
# copy from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava_onevision/image_processing_llava_onevision_fast.py
|
||||
from typing import Optional
|
||||
|
||||
from transformers.image_processing_utils import (
|
||||
BatchFeature,
|
||||
get_patch_output_size,
|
||||
@@ -164,11 +165,11 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
|
||||
|
||||
def _resize_for_patching(
|
||||
self,
|
||||
image: torch.Tensor,
|
||||
image: "torch.Tensor",
|
||||
target_resolution: tuple,
|
||||
interpolation: F.InterpolationMode,
|
||||
interpolation: "F.InterpolationMode",
|
||||
input_data_format: ChannelDimension,
|
||||
) -> torch.Tensor:
|
||||
) -> "torch.Tensor":
|
||||
"""
|
||||
Resizes an image to a target resolution while maintaining aspect ratio.
|
||||
|
||||
@@ -218,8 +219,8 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
|
||||
return best_ratio
|
||||
|
||||
def _pad_for_patching(
|
||||
self, image: torch.Tensor, target_resolution: tuple, input_data_format: ChannelDimension
|
||||
) -> torch.Tensor:
|
||||
self, image: "torch.Tensor", target_resolution: tuple, input_data_format: ChannelDimension
|
||||
) -> "torch.Tensor":
|
||||
"""
|
||||
Pad an image to a target resolution while maintaining aspect ratio.
|
||||
"""
|
||||
@@ -235,15 +236,15 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
|
||||
|
||||
def _get_image_patches(
|
||||
self,
|
||||
image: torch.Tensor,
|
||||
image: "torch.Tensor",
|
||||
min_num: int,
|
||||
max_num: int,
|
||||
size: tuple,
|
||||
tile_size: int,
|
||||
use_thumbnail: bool,
|
||||
interpolation: F.InterpolationMode,
|
||||
interpolation: "F.InterpolationMode",
|
||||
pad_during_tiling: bool,
|
||||
) -> list[torch.Tensor]:
|
||||
) -> list["torch.Tensor"]:
|
||||
image_size = get_image_size(image, channel_dim=ChannelDimension.FIRST)
|
||||
orig_height, orig_width = image_size
|
||||
aspect_ratio = orig_width / orig_height
|
||||
@@ -304,8 +305,8 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
|
||||
|
||||
def _pad_for_batching(
|
||||
self,
|
||||
pixel_values: list[torch.Tensor],
|
||||
) -> list[torch.Tensor]:
|
||||
pixel_values: list["torch.Tensor"],
|
||||
) -> list["torch.Tensor"]:
|
||||
"""
|
||||
Pads images on the `num_of_patches` dimension with zeros to form a batch of same number of patches.
|
||||
|
||||
@@ -326,14 +327,14 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
|
||||
|
||||
def _preprocess(
|
||||
self,
|
||||
images: list[torch.Tensor],
|
||||
images: list["torch.Tensor"],
|
||||
do_resize: bool,
|
||||
size: SizeDict,
|
||||
max_dynamic_tiles: int,
|
||||
min_dynamic_tiles: int,
|
||||
use_thumbnail: bool,
|
||||
pad_during_tiling: bool,
|
||||
interpolation: F.InterpolationMode | None,
|
||||
interpolation: Optional["F.InterpolationMode"],
|
||||
do_center_crop: bool,
|
||||
crop_size: SizeDict,
|
||||
do_rescale: bool,
|
||||
|
||||
@@ -49,7 +49,7 @@ from lerobot.processor.converters import (
|
||||
policy_action_to_transition,
|
||||
transition_to_policy_action,
|
||||
)
|
||||
from lerobot.types import EnvTransition, TransitionKey
|
||||
from lerobot.processor.core import EnvTransition, TransitionKey
|
||||
from lerobot.utils.constants import (
|
||||
ACTION,
|
||||
HF_LEROBOT_HOME,
|
||||
|
||||
@@ -20,11 +20,12 @@ import logging
|
||||
import math
|
||||
from collections import deque
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, Literal, TypedDict, Unpack
|
||||
from typing import TYPE_CHECKING, Literal, TypedDict
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F # noqa: N812
|
||||
from torch import Tensor, nn
|
||||
from typing_extensions import Unpack
|
||||
|
||||
from lerobot.utils.import_utils import _transformers_available
|
||||
|
||||
|
||||
@@ -20,11 +20,12 @@ import logging
|
||||
import math
|
||||
from collections import deque
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, Literal, TypedDict, Unpack
|
||||
from typing import TYPE_CHECKING, Literal, TypedDict
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F # noqa: N812
|
||||
from torch import Tensor, nn
|
||||
from typing_extensions import Unpack
|
||||
|
||||
from lerobot.utils.import_utils import _transformers_available
|
||||
|
||||
|
||||
@@ -36,7 +36,7 @@ from lerobot.processor import (
|
||||
UnnormalizerProcessorStep,
|
||||
)
|
||||
from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action
|
||||
from lerobot.types import EnvTransition, TransitionKey
|
||||
from lerobot.processor.core import EnvTransition, TransitionKey
|
||||
from lerobot.utils.constants import (
|
||||
OBS_STATE,
|
||||
POLICY_POSTPROCESSOR_DEFAULT_NAME,
|
||||
|
||||
@@ -19,12 +19,13 @@ import logging
|
||||
import math
|
||||
from collections import deque
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, Literal, TypedDict, Unpack
|
||||
from typing import TYPE_CHECKING, Literal, TypedDict
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F # noqa: N812
|
||||
from torch import Tensor, nn
|
||||
from typing_extensions import Unpack
|
||||
|
||||
from lerobot.utils.import_utils import _scipy_available, _transformers_available
|
||||
|
||||
|
||||
@@ -37,7 +37,7 @@ from lerobot.processor import (
|
||||
UnnormalizerProcessorStep,
|
||||
)
|
||||
from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action
|
||||
from lerobot.types import EnvTransition, TransitionKey
|
||||
from lerobot.processor.core import EnvTransition, TransitionKey
|
||||
from lerobot.utils.constants import (
|
||||
OBS_STATE,
|
||||
POLICY_POSTPROCESSOR_DEFAULT_NAME,
|
||||
|
||||
@@ -19,7 +19,7 @@ import os
|
||||
from importlib.resources import files
|
||||
from pathlib import Path
|
||||
from tempfile import TemporaryDirectory
|
||||
from typing import TypedDict, TypeVar, Unpack
|
||||
from typing import TypedDict, TypeVar
|
||||
|
||||
import packaging
|
||||
import safetensors
|
||||
@@ -28,6 +28,7 @@ from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
|
||||
from huggingface_hub.errors import HfHubHTTPError
|
||||
from safetensors.torch import load_model as load_model_as_safetensor, save_model as save_model_as_safetensor
|
||||
from torch import Tensor, nn
|
||||
from typing_extensions import Unpack
|
||||
|
||||
from lerobot.configs.policies import PreTrainedConfig
|
||||
from lerobot.configs.train import TrainPipelineConfig
|
||||
|
||||
@@ -48,8 +48,8 @@ from lerobot.processor.converters import (
|
||||
policy_action_to_transition,
|
||||
transition_to_policy_action,
|
||||
)
|
||||
from lerobot.processor.core import EnvTransition, TransitionKey
|
||||
from lerobot.processor.pipeline import PipelineFeatureType
|
||||
from lerobot.types import EnvTransition, TransitionKey
|
||||
from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME
|
||||
|
||||
|
||||
|
||||
@@ -54,11 +54,12 @@ policy = SmolVLAPolicy.from_pretrained("lerobot/smolvla_base")
|
||||
|
||||
import math
|
||||
from collections import deque
|
||||
from typing import TypedDict, Unpack
|
||||
from typing import TypedDict
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F # noqa: N812
|
||||
from torch import Tensor, nn
|
||||
from typing_extensions import Unpack
|
||||
|
||||
from lerobot.policies.pretrained import PreTrainedPolicy
|
||||
from lerobot.policies.rtc.modeling_rtc import RTCProcessor
|
||||
@@ -68,7 +69,7 @@ from lerobot.policies.utils import (
|
||||
populate_queues,
|
||||
)
|
||||
from lerobot.utils.constants import ACTION, OBS_LANGUAGE_ATTENTION_MASK, OBS_LANGUAGE_TOKENS, OBS_STATE
|
||||
from lerobot.utils.device_utils import get_safe_dtype
|
||||
from lerobot.utils.utils import get_safe_dtype
|
||||
|
||||
|
||||
class ActionSelectKwargs(TypedDict, total=False):
|
||||
@@ -374,11 +375,9 @@ class SmolVLAPolicy(PreTrainedPolicy):
|
||||
lang_tokens = batch[f"{OBS_LANGUAGE_TOKENS}"]
|
||||
lang_masks = batch[f"{OBS_LANGUAGE_ATTENTION_MASK}"]
|
||||
actions = self.prepare_action(batch)
|
||||
actions_is_pad = batch.get("action_is_pad")
|
||||
actions_is_pad = batch.get("actions_id_pad")
|
||||
loss_dict = {}
|
||||
losses = self.model.forward(images, img_masks, lang_tokens, lang_masks, state, actions, noise, time)
|
||||
original_action_dim = self.config.action_feature.shape[0]
|
||||
losses = losses[:, :, :original_action_dim]
|
||||
loss_dict["losses_after_forward"] = losses.clone().mean().item()
|
||||
|
||||
if actions_is_pad is not None:
|
||||
|
||||
@@ -23,8 +23,8 @@ from torch import nn
|
||||
|
||||
from lerobot.configs.policies import PreTrainedConfig
|
||||
from lerobot.configs.types import FeatureType, PolicyFeature
|
||||
from lerobot.datasets.feature_utils import build_dataset_frame
|
||||
from lerobot.types import PolicyAction, RobotAction, RobotObservation
|
||||
from lerobot.datasets.utils import build_dataset_frame
|
||||
from lerobot.processor import PolicyAction, RobotAction, RobotObservation
|
||||
from lerobot.utils.constants import ACTION, OBS_STR
|
||||
|
||||
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user