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1 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Steven Palma
|
2e2c27da34 |
@@ -1,86 +0,0 @@
|
||||
# LeRobot — Claude Code Instructions
|
||||
|
||||
You are a senior robotics ML engineer reviewing code for **LeRobot**, a PyTorch framework for real-world robot learning.
|
||||
Apply these principles to every PR review, fix, or task.
|
||||
|
||||
---
|
||||
|
||||
## Core Abstractions
|
||||
|
||||
These are the load-bearing types. Handle them with care — breaking changes here affect every user.
|
||||
|
||||
| Type | Location | Role |
|
||||
| ---------------- | ---------------------------- | ------------------------------------------------------------ |
|
||||
| `LeRobotDataset` | `src/lerobot/datasets/` | Streaming replay buffer; HF Hub integration |
|
||||
| `Policy` | `src/lerobot/policies/` | Base class for all learning agents (ACT, Diffusion, SARM, …) |
|
||||
| `Robot` | `src/lerobot/robots/` | Hardware abstraction; carries `_output_pipeline` |
|
||||
| `Teleoperator` | `src/lerobot/teleoperators/` | Leader-side hardware abstraction; carries `_output_pipeline` |
|
||||
| `Env` | `src/lerobot/envs/` | Gym-like robotics environments |
|
||||
| `Processor` | `src/lerobot/processor/` | Data transformation pipelines attached to robots/teleops |
|
||||
|
||||
**Never break their public APIs without a migration note and explicit user approval.**
|
||||
|
||||
---
|
||||
|
||||
## Engineering Principles
|
||||
|
||||
### Code quality
|
||||
|
||||
- Explicit over magic — no hidden control flow, no implicit state.
|
||||
- No deep inheritance trees. Prefer composition.
|
||||
- No decorative comment separators (`===`, `---`, etc.).
|
||||
- Add comments only where the logic is non-obvious.
|
||||
- No over-engineering. YAGNI applies strictly.
|
||||
|
||||
### Type safety
|
||||
|
||||
- All new and modified Python code must be fully typed (PEP 484).
|
||||
- `mypy --strict` must pass on changed files.
|
||||
- Do not widen or weaken existing type signatures.
|
||||
|
||||
### Backwards compatibility
|
||||
|
||||
- Public API changes require migration notes.
|
||||
- Additive changes are preferred over modifications.
|
||||
- `so100_follower` / `so101_follower` are aliases — never bleed changes there unintentionally.
|
||||
|
||||
### HF ecosystem
|
||||
|
||||
- Use `push_to_hub()`, HF Hub dataset streaming, and `evaluate` scripts.
|
||||
- Dataset changes must preserve streaming compatibility.
|
||||
- Prefer reusing HF primitives over rolling custom solutions.
|
||||
|
||||
---
|
||||
|
||||
## PR Review Checklist
|
||||
|
||||
Before approving or marking P1 issues resolved, verify:
|
||||
|
||||
- [ ] `pre-commit run -a` would pass (ruff, mypy, typos, zizmor, bandit)
|
||||
- [ ] All new/modified code is typed and passes `mypy --strict`
|
||||
- [ ] New features have unit tests; no silent behavioral changes
|
||||
- [ ] Public APIs of `LeRobotDataset`, `Policy`, `Robot`, `Teleoperator`, `Env` are unchanged (or migration note present)
|
||||
- [ ] HF Hub streaming still works for dataset changes
|
||||
- [ ] No unnecessary abstractions introduced
|
||||
- [ ] No breaking changes to training scripts (`lerobot-train`, `lerobot-eval`, `lerobot-record`)
|
||||
|
||||
---
|
||||
|
||||
## ML-Specific Checks
|
||||
|
||||
Flag these as **P1** if found:
|
||||
|
||||
- **Data leakage**: train and val/test splits must be constructed before any normalization or augmentation that uses train statistics.
|
||||
- **Loss function errors**: verify reduction mode (`mean` vs `sum`), correct masking, correct shape alignment.
|
||||
- **Gradient flow**: new modules must have gradients flowing (check `requires_grad`, no detached tensors in the loss path by accident).
|
||||
- **Distributed training**: operations on tensors must be DDP-safe; no in-place ops on parameters; batch norm needs `SyncBatchNorm` if used.
|
||||
- **Memory leaks**: no accumulation of tensors outside the training loop; `optimizer.zero_grad()` called correctly.
|
||||
|
||||
---
|
||||
|
||||
## What to Skip
|
||||
|
||||
- Don't flag style nitpicks on unchanged surrounding code.
|
||||
- Don't propose refactors outside the PR's scope.
|
||||
- Don't add docstrings or comments to code the PR didn't touch.
|
||||
- Don't suggest speculative future features (YAGNI).
|
||||
@@ -1,49 +0,0 @@
|
||||
name: Claude Code Review
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
types: [opened, synchronize, ready_for_review, reopened]
|
||||
|
||||
jobs:
|
||||
claude-review:
|
||||
runs-on: ubuntu-latest
|
||||
permissions:
|
||||
contents: read
|
||||
pull-requests: write
|
||||
issues: read
|
||||
id-token: write
|
||||
actions: read
|
||||
env:
|
||||
FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true
|
||||
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 1
|
||||
persist-credentials: false
|
||||
|
||||
- name: Run Claude Code Review
|
||||
id: claude-review
|
||||
uses: anthropics/claude-code-action@26ddc358fe3befff50c5ec2f80304c90c763f6f8 # v1
|
||||
with:
|
||||
anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
|
||||
use_sticky_comment: true
|
||||
prompt: |
|
||||
Read `.github/CLAUDE.md` for lerobot-specific conventions, then review this PR.
|
||||
Provide structured, actionable feedback.
|
||||
|
||||
Focus areas (in priority order):
|
||||
1. **Correctness**: Logic errors, off-by-ones, wrong tensor shapes, incorrect loss functions
|
||||
2. **Type safety**: All new/modified Python code must pass `mypy --strict`; check for missing annotations
|
||||
3. **Backwards compatibility**: Does this break `LeRobotDataset`, `Policy`, `Robot`, `Teleoperator`, `Env`, or `Processor` public APIs?
|
||||
4. **Tests**: New features must have tests; no silent behavioral changes
|
||||
5. **Code style**: Explicit over magic, no unnecessary abstractions, no decorative comments
|
||||
6. **HF integration**: Dataset streaming, `push_to_hub`, HF Hub compatibility preserved?
|
||||
7. **pre-commit**: Would `pre-commit run -a` pass? (ruff, mypy, typos, zizmor)
|
||||
|
||||
Format findings as P1 (must fix) / P2 (should fix) / P3 (nice to have).
|
||||
Skip P3 if the PR is already high quality.
|
||||
claude_args: '--model claude-opus-4-6'
|
||||
# See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md
|
||||
# or https://code.claude.com/docs/en/cli-reference for available options
|
||||
@@ -1,58 +0,0 @@
|
||||
name: Claude Code
|
||||
|
||||
on:
|
||||
issue_comment:
|
||||
types: [created]
|
||||
pull_request_review_comment:
|
||||
types: [created]
|
||||
issues:
|
||||
types: [opened, assigned]
|
||||
pull_request_review:
|
||||
types: [submitted]
|
||||
|
||||
jobs:
|
||||
claude:
|
||||
if: |
|
||||
(github.event_name == 'issue_comment' &&
|
||||
contains(github.event.comment.body, '@claude') &&
|
||||
(github.event.comment.author_association == 'OWNER' || github.event.comment.author_association == 'MEMBER' || github.event.comment.author_association == 'COLLABORATOR')) ||
|
||||
(github.event_name == 'pull_request_review_comment' &&
|
||||
contains(github.event.comment.body, '@claude') &&
|
||||
(github.event.comment.author_association == 'OWNER' || github.event.comment.author_association == 'MEMBER' || github.event.comment.author_association == 'COLLABORATOR')) ||
|
||||
(github.event_name == 'pull_request_review' &&
|
||||
contains(github.event.review.body, '@claude') &&
|
||||
(github.event.review.author_association == 'OWNER' || github.event.review.author_association == 'MEMBER' || github.event.review.author_association == 'COLLABORATOR')) ||
|
||||
(github.event_name == 'issues' &&
|
||||
(contains(github.event.issue.body, '@claude') || contains(github.event.issue.title, '@claude')) &&
|
||||
(github.event.issue.author_association == 'OWNER' || github.event.issue.author_association == 'MEMBER' || github.event.issue.author_association == 'COLLABORATOR'))
|
||||
runs-on: ubuntu-latest
|
||||
permissions:
|
||||
contents: read
|
||||
pull-requests: write
|
||||
issues: write
|
||||
id-token: write
|
||||
actions: read
|
||||
env:
|
||||
FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true
|
||||
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 1
|
||||
persist-credentials: false
|
||||
|
||||
- name: Run Claude Code
|
||||
id: claude
|
||||
uses: anthropics/claude-code-action@26ddc358fe3befff50c5ec2f80304c90c763f6f8 # v1
|
||||
with:
|
||||
anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
|
||||
use_sticky_comment: true
|
||||
|
||||
# This is an optional setting that allows Claude to read CI results on PRs
|
||||
additional_permissions: |
|
||||
actions: read
|
||||
|
||||
claude_args: '--system-prompt "Read .github/CLAUDE.md for lerobot-specific conventions before responding."'
|
||||
# See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md
|
||||
# or https://code.claude.com/docs/en/cli-reference for available options
|
||||
@@ -33,7 +33,7 @@ jobs:
|
||||
github.event.workflow_run.event == 'pull_request' &&
|
||||
github.event.workflow_run.conclusion == 'success' &&
|
||||
github.repository == 'huggingface/lerobot'
|
||||
uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@90b4ee2c10b81b5c1a6367c4e6fc9e2fb510a7e3 # main
|
||||
uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@main
|
||||
with:
|
||||
package_name: lerobot
|
||||
secrets:
|
||||
|
||||
@@ -55,7 +55,7 @@ jobs:
|
||||
github.repository == 'huggingface/lerobot'
|
||||
permissions:
|
||||
contents: read
|
||||
uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@90b4ee2c10b81b5c1a6367c4e6fc9e2fb510a7e3 # main
|
||||
uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main
|
||||
with:
|
||||
commit_sha: ${{ github.sha }}
|
||||
package: lerobot
|
||||
@@ -78,7 +78,7 @@ jobs:
|
||||
permissions:
|
||||
contents: read
|
||||
pull-requests: write
|
||||
uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@90b4ee2c10b81b5c1a6367c4e6fc9e2fb510a7e3 # main
|
||||
uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
|
||||
with:
|
||||
commit_sha: ${{ github.event.pull_request.head.sha }}
|
||||
pr_number: ${{ github.event.number }}
|
||||
|
||||
@@ -65,7 +65,7 @@ jobs:
|
||||
HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
|
||||
HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
|
||||
steps:
|
||||
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
|
||||
- uses: actions/checkout@v6
|
||||
with:
|
||||
persist-credentials: false
|
||||
lfs: true
|
||||
@@ -83,7 +83,7 @@ jobs:
|
||||
libusb-1.0-0-dev speech-dispatcher libgeos-dev portaudio19-dev
|
||||
|
||||
- name: Setup uv and Python
|
||||
uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
|
||||
uses: astral-sh/setup-uv@v6 # zizmor: ignore[unpinned-uses]
|
||||
with:
|
||||
enable-cache: true
|
||||
version: ${{ env.UV_VERSION }}
|
||||
|
||||
@@ -63,7 +63,7 @@ jobs:
|
||||
HF_LEROBOT_HOME: /mnt/cache/.cache/huggingface/lerobot
|
||||
HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
|
||||
steps:
|
||||
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
|
||||
- uses: actions/checkout@v6
|
||||
with:
|
||||
lfs: true
|
||||
persist-credentials: false
|
||||
@@ -80,7 +80,7 @@ jobs:
|
||||
speech-dispatcher libgeos-dev portaudio19-dev
|
||||
|
||||
- name: Setup uv and Python
|
||||
uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
|
||||
uses: astral-sh/setup-uv@v6 # zizmor: ignore[unpinned-uses]
|
||||
with:
|
||||
enable-cache: true
|
||||
version: ${{ env.UV_VERSION }}
|
||||
@@ -137,21 +137,21 @@ jobs:
|
||||
sudo apt-get update
|
||||
sudo apt-get install git-lfs
|
||||
git lfs install
|
||||
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
|
||||
- uses: actions/checkout@v6
|
||||
with:
|
||||
lfs: true
|
||||
persist-credentials: false
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3
|
||||
uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
|
||||
with:
|
||||
cache-binary: false
|
||||
- name: Login to Docker Hub
|
||||
uses: docker/login-action@c94ce9fb468520275223c153574b00df6fe4bcc9 # v3
|
||||
uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
|
||||
with:
|
||||
username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
|
||||
password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
|
||||
- name: Build and push Docker image
|
||||
uses: docker/build-push-action@10e90e3645eae34f1e60eeb005ba3a3d33f178e8 # v6
|
||||
uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
|
||||
with:
|
||||
context: .
|
||||
file: ./docker/Dockerfile.internal
|
||||
|
||||
@@ -227,7 +227,7 @@ jobs:
|
||||
contents: write
|
||||
pull-requests: write
|
||||
env:
|
||||
GH_TOKEN: ${{ secrets.UPDATE_LOCK_TOKEN }}
|
||||
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||
steps:
|
||||
- uses: actions/checkout@v6
|
||||
with:
|
||||
|
||||
@@ -43,16 +43,16 @@ jobs:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
|
||||
uses: actions/checkout@v6
|
||||
with:
|
||||
persist-credentials: false
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6
|
||||
uses: actions/setup-python@v6
|
||||
with:
|
||||
python-version: '3.12'
|
||||
|
||||
- name: Run pre-commit hooks
|
||||
uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1
|
||||
uses: pre-commit/action@v3.0.1 # zizmor: ignore[unpinned-uses]
|
||||
with:
|
||||
extra_args: --all-files --show-diff-on-failure --color=always
|
||||
|
||||
@@ -38,12 +38,12 @@ jobs:
|
||||
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
|
||||
uses: actions/checkout@v6
|
||||
with:
|
||||
persist-credentials: false
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6
|
||||
uses: actions/setup-python@v6
|
||||
with:
|
||||
python-version: '3.12'
|
||||
|
||||
@@ -104,7 +104,7 @@ jobs:
|
||||
- name: Publish to TestPyPI for pre-releases
|
||||
# True for tags like 'v0.2.0-rc1'
|
||||
if: startsWith(github.ref, 'refs/tags/v') && contains(github.ref, '-')
|
||||
uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e # v1.13.0
|
||||
uses: pypa/gh-action-pypi-publish@v1.13.0 # zizmor: ignore[unpinned-uses, use-trusted-publishing]
|
||||
with:
|
||||
repository-url: https://test.pypi.org/legacy/
|
||||
verbose: true
|
||||
@@ -112,7 +112,7 @@ jobs:
|
||||
|
||||
- name: Publish to PyPI
|
||||
if: startsWith(github.ref, 'refs/tags/v') && !contains(github.ref, '-')
|
||||
uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e # v1.13.0
|
||||
uses: pypa/gh-action-pypi-publish@v1.13.0 # zizmor: ignore[unpinned-uses, use-trusted-publishing]
|
||||
with:
|
||||
verbose: true
|
||||
print-hash: true
|
||||
@@ -127,7 +127,7 @@ jobs:
|
||||
env:
|
||||
MUJOCO_GL: egl
|
||||
steps:
|
||||
- uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
|
||||
- uses: actions/checkout@v6
|
||||
with:
|
||||
lfs: true
|
||||
persist-credentials: false
|
||||
@@ -137,7 +137,7 @@ jobs:
|
||||
git curl libglib2.0-0 libegl1-mesa-dev ffmpeg libusb-1.0-0-dev \
|
||||
speech-dispatcher libgeos-dev portaudio19-dev
|
||||
- name: Setup uv and Python
|
||||
uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
|
||||
uses: astral-sh/setup-uv@v6 # zizmor: ignore[unpinned-uses]
|
||||
with:
|
||||
enable-cache: true # zizmor: ignore[cache-poisoning]
|
||||
version: ${{ env.UV_VERSION }}
|
||||
|
||||
@@ -43,12 +43,12 @@ jobs:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
|
||||
uses: actions/checkout@v6 # zizmor: ignore[unpinned-uses]
|
||||
with:
|
||||
fetch-depth: 0
|
||||
persist-credentials: false
|
||||
|
||||
- name: Secret Scanning
|
||||
uses: trufflesecurity/trufflehog@eafb8c5f6a06175141c27f17bcc17941853d0047 # v3.90.0
|
||||
uses: trufflesecurity/trufflehog@v3.90.0 # zizmor: ignore[unpinned-uses]
|
||||
with:
|
||||
extra_args: --only-verified
|
||||
|
||||
@@ -4,7 +4,6 @@
|
||||
|
||||
<div align="center">
|
||||
|
||||
[](https://github.com/huggingface/lerobot/actions/workflows/latest_deps_tests.yml?query=branch%3Amain)
|
||||
[](https://github.com/huggingface/lerobot/actions/workflows/docker_publish.yml?query=branch%3Amain)
|
||||
[](https://www.python.org/downloads/)
|
||||
[](https://github.com/huggingface/lerobot/blob/main/LICENSE)
|
||||
|
||||
@@ -115,22 +115,23 @@ Each `EnvConfig` subclass declares two dicts that tell the policy what to expect
|
||||
## Step by step
|
||||
|
||||
<Tip>
|
||||
At minimum, you need two files: a **gym.Env wrapper** and an **EnvConfig
|
||||
subclass** with a `create_envs()` override. Everything else is optional or
|
||||
documentation. No changes to `factory.py` are needed.
|
||||
At minimum, you need three files: a **gym.Env wrapper**, an **EnvConfig
|
||||
subclass**, and a **factory dispatch branch**. Everything else is optional or
|
||||
documentation.
|
||||
</Tip>
|
||||
|
||||
### Checklist
|
||||
|
||||
| File | Required | Why |
|
||||
| ---------------------------------------- | -------- | ------------------------------------------------------------ |
|
||||
| `src/lerobot/envs/<benchmark>.py` | Yes | Wraps the simulator as a standard gym.Env |
|
||||
| `src/lerobot/envs/configs.py` | Yes | Registers your benchmark and its `create_envs()` for the CLI |
|
||||
| `src/lerobot/processor/env_processor.py` | Optional | Custom observation/action transforms |
|
||||
| `src/lerobot/envs/utils.py` | Optional | Only if you need new raw observation keys |
|
||||
| `pyproject.toml` | Yes | Declares benchmark-specific dependencies |
|
||||
| `docs/source/<benchmark>.mdx` | Yes | User-facing documentation page |
|
||||
| `docs/source/_toctree.yml` | Yes | Adds your page to the docs sidebar |
|
||||
| File | Required | Why |
|
||||
| ---------------------------------------- | -------- | ----------------------------------------- |
|
||||
| `src/lerobot/envs/<benchmark>.py` | Yes | Wraps the simulator as a standard gym.Env |
|
||||
| `src/lerobot/envs/configs.py` | Yes | Registers your benchmark for the CLI |
|
||||
| `src/lerobot/envs/factory.py` | Yes | Tells `make_env()` how to build your envs |
|
||||
| `src/lerobot/processor/env_processor.py` | Optional | Custom observation/action transforms |
|
||||
| `src/lerobot/envs/utils.py` | Optional | Only if you need new raw observation keys |
|
||||
| `pyproject.toml` | Yes | Declares benchmark-specific dependencies |
|
||||
| `docs/source/<benchmark>.mdx` | Yes | User-facing documentation page |
|
||||
| `docs/source/_toctree.yml` | Yes | Adds your page to the docs sidebar |
|
||||
|
||||
### 1. The gym.Env wrapper (`src/lerobot/envs/<benchmark>.py`)
|
||||
|
||||
@@ -178,10 +179,7 @@ See `create_libero_envs()` (multi-suite, multi-task) and `create_metaworld_envs(
|
||||
|
||||
### 2. The config (`src/lerobot/envs/configs.py`)
|
||||
|
||||
Register a config dataclass so users can select your benchmark with `--env.type=<name>`. Each config owns its environment creation and processor logic via two methods:
|
||||
|
||||
- **`create_envs(n_envs, use_async_envs)`** — Returns `{suite: {task_id: VectorEnv}}`. The base class default uses `gym.make()` for single-task envs. Multi-task benchmarks override this.
|
||||
- **`get_env_processors()`** — Returns `(preprocessor, postprocessor)`. The base class default returns identity (no-op) pipelines. Override if your benchmark needs observation/action transforms.
|
||||
Register a config dataclass so users can select your benchmark with `--env.type=<name>`:
|
||||
|
||||
```python
|
||||
@EnvConfig.register_subclass("<benchmark_name>")
|
||||
@@ -206,20 +204,6 @@ class MyBenchmarkEnvConfig(EnvConfig):
|
||||
@property
|
||||
def gym_kwargs(self) -> dict:
|
||||
return {"obs_type": self.obs_type, "render_mode": self.render_mode}
|
||||
|
||||
def create_envs(self, n_envs: int, use_async_envs: bool = False):
|
||||
"""Override for multi-task benchmarks or custom env creation."""
|
||||
from lerobot.envs.<benchmark> import create_<benchmark>_envs
|
||||
return create_<benchmark>_envs(task=self.task, n_envs=n_envs, ...)
|
||||
|
||||
def get_env_processors(self):
|
||||
"""Override if your benchmark needs observation/action transforms."""
|
||||
from lerobot.processor.pipeline import PolicyProcessorPipeline
|
||||
from lerobot.processor.env_processor import MyBenchmarkProcessorStep
|
||||
return (
|
||||
PolicyProcessorPipeline(steps=[MyBenchmarkProcessorStep()]),
|
||||
PolicyProcessorPipeline(steps=[]),
|
||||
)
|
||||
```
|
||||
|
||||
Key points:
|
||||
@@ -227,11 +211,36 @@ Key points:
|
||||
- The `register_subclass` name is what users pass on the CLI (`--env.type=<name>`).
|
||||
- `features` tells the policy what the environment produces.
|
||||
- `features_map` maps raw observation keys to LeRobot convention keys.
|
||||
- **No changes to `factory.py` needed** — the factory delegates to `cfg.create_envs()` and `cfg.get_env_processors()` automatically.
|
||||
|
||||
### 3. Env processor (optional — `src/lerobot/processor/env_processor.py`)
|
||||
### 3. The factory dispatch (`src/lerobot/envs/factory.py`)
|
||||
|
||||
Only needed if your benchmark requires observation transforms beyond what `preprocess_observation()` handles (e.g. image flipping, coordinate conversion). Define the processor step here and return it from `get_env_processors()` in your config (see step 2):
|
||||
Add a branch in `make_env()` to call your factory function:
|
||||
|
||||
```python
|
||||
elif "<benchmark_name>" in cfg.type:
|
||||
from lerobot.envs.<benchmark> import create_<benchmark>_envs
|
||||
|
||||
if cfg.task is None:
|
||||
raise ValueError("<BenchmarkName> requires a task to be specified")
|
||||
|
||||
return create_<benchmark>_envs(
|
||||
task=cfg.task,
|
||||
n_envs=n_envs,
|
||||
gym_kwargs=cfg.gym_kwargs,
|
||||
env_cls=env_cls,
|
||||
)
|
||||
```
|
||||
|
||||
If your benchmark needs an env processor, add it in `make_env_pre_post_processors()`:
|
||||
|
||||
```python
|
||||
if isinstance(env_cfg, MyBenchmarkEnvConfig) or "<benchmark_name>" in env_cfg.type:
|
||||
preprocessor_steps.append(MyBenchmarkProcessorStep())
|
||||
```
|
||||
|
||||
### 4. Env processor (optional — `src/lerobot/processor/env_processor.py`)
|
||||
|
||||
Only needed if your benchmark requires observation transforms beyond what `preprocess_observation()` handles (e.g. image flipping, coordinate conversion):
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
@@ -251,7 +260,7 @@ class MyBenchmarkProcessorStep(ObservationProcessorStep):
|
||||
|
||||
See `LiberoProcessorStep` for a full example (image rotation, quaternion-to-axis-angle conversion).
|
||||
|
||||
### 4. Dependencies (`pyproject.toml`)
|
||||
### 5. Dependencies (`pyproject.toml`)
|
||||
|
||||
Add a new optional-dependency group:
|
||||
|
||||
@@ -272,11 +281,11 @@ Users install with:
|
||||
pip install -e ".[mybenchmark]"
|
||||
```
|
||||
|
||||
### 5. Documentation (`docs/source/<benchmark>.mdx`)
|
||||
### 6. Documentation (`docs/source/<benchmark>.mdx`)
|
||||
|
||||
Write a user-facing page following the template in the next section. See `docs/source/libero.mdx` and `docs/source/metaworld.mdx` for full examples.
|
||||
|
||||
### 6. Table of contents (`docs/source/_toctree.yml`)
|
||||
### 7. Table of contents (`docs/source/_toctree.yml`)
|
||||
|
||||
Add your benchmark to the "Benchmarks" section:
|
||||
|
||||
|
||||
@@ -90,17 +90,11 @@ The same policy can work with different environment processors, and the same env
|
||||
|
||||
```python
|
||||
# Use SmolVLA policy with LIBERO environment
|
||||
# Use SmolVLA policy with LIBERO environment
|
||||
libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(
|
||||
env_cfg=libero_cfg,
|
||||
policy_cfg=smolvla_cfg,
|
||||
)
|
||||
libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(libero_cfg)
|
||||
smolvla_preprocessor, smolvla_postprocessor = make_pre_post_processors(smolvla_cfg)
|
||||
|
||||
# Or use ACT policy with the same LIBERO environment
|
||||
libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(
|
||||
env_cfg=libero_cfg,
|
||||
policy_cfg=act_cfg,
|
||||
)
|
||||
libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(libero_cfg)
|
||||
act_preprocessor, act_postprocessor = make_pre_post_processors(act_cfg)
|
||||
```
|
||||
|
||||
@@ -157,7 +151,7 @@ observation = {
|
||||
|
||||
### Factory Function
|
||||
|
||||
The `make_env_pre_post_processors` function delegates to `env_cfg.get_env_processors()`:
|
||||
The `make_env_pre_post_processors` function follows the same pattern as `make_pre_post_processors` for policies:
|
||||
|
||||
```python
|
||||
from lerobot.envs.factory import make_env_pre_post_processors
|
||||
@@ -165,30 +159,46 @@ from lerobot.envs.configs import LiberoEnv, PushtEnv
|
||||
|
||||
# For LIBERO: Returns LiberoProcessorStep in preprocessor
|
||||
libero_cfg = LiberoEnv(task="libero_spatial", camera_name=["agentview"])
|
||||
env_preprocessor, env_postprocessor = make_env_pre_post_processors(libero_cfg, policy_cfg)
|
||||
env_preprocessor, env_postprocessor = make_env_pre_post_processors(libero_cfg)
|
||||
|
||||
# For other environments: Returns identity processors (no-op)
|
||||
pusht_cfg = PushtEnv()
|
||||
env_preprocessor, env_postprocessor = make_env_pre_post_processors(pusht_cfg, policy_cfg)
|
||||
env_preprocessor, env_postprocessor = make_env_pre_post_processors(pusht_cfg)
|
||||
```
|
||||
|
||||
### How It Works
|
||||
|
||||
Each `EnvConfig` subclass can override `get_env_processors()` to return benchmark-specific
|
||||
processor pipelines. The base class returns identity (no-op) processors by default.
|
||||
### Implementation in `envs/factory.py`
|
||||
|
||||
```python
|
||||
# In your EnvConfig subclass:
|
||||
def get_env_processors(self):
|
||||
from lerobot.processor.pipeline import PolicyProcessorPipeline
|
||||
return (
|
||||
PolicyProcessorPipeline(steps=[MyProcessorStep()]),
|
||||
PolicyProcessorPipeline(steps=[]),
|
||||
)
|
||||
```
|
||||
def make_env_pre_post_processors(
|
||||
env_cfg: EnvConfig,
|
||||
) -> tuple[
|
||||
PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
|
||||
PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
|
||||
]:
|
||||
"""
|
||||
Create preprocessor and postprocessor pipelines for environment observations.
|
||||
|
||||
The factory function `make_env_pre_post_processors` simply delegates to this method,
|
||||
with a special case for `XVLAConfig` policies which override the env processors entirely.
|
||||
Args:
|
||||
env_cfg: The configuration of the environment.
|
||||
|
||||
Returns:
|
||||
A tuple containing:
|
||||
- preprocessor: Pipeline that processes environment observations
|
||||
- postprocessor: Pipeline that processes environment outputs
|
||||
"""
|
||||
# For LIBERO environments, add the LiberoProcessorStep to preprocessor
|
||||
if isinstance(env_cfg, LiberoEnv) or "libero" in env_cfg.type:
|
||||
preprocessor = PolicyProcessorPipeline(steps=[LiberoProcessorStep()])
|
||||
else:
|
||||
# For all other environments, return an identity preprocessor
|
||||
preprocessor = PolicyProcessorPipeline(steps=[])
|
||||
|
||||
# Postprocessor is currently identity for all environments
|
||||
# Future: Could add environment-specific action transformations
|
||||
postprocessor = PolicyProcessorPipeline(steps=[])
|
||||
|
||||
return preprocessor, postprocessor
|
||||
```
|
||||
|
||||
### Integration in Evaluation
|
||||
|
||||
@@ -209,10 +219,7 @@ def eval_main(cfg: EvalPipelineConfig):
|
||||
)
|
||||
|
||||
# Create environment processors (NEW!)
|
||||
env_preprocessor, env_postprocessor = make_env_pre_post_processors(
|
||||
env_cfg=cfg.env,
|
||||
policy_cfg=cfg.policy,
|
||||
)
|
||||
env_preprocessor, env_postprocessor = make_env_pre_post_processors(env_cfg=cfg.env)
|
||||
|
||||
# Run evaluation with both processor types
|
||||
eval_policy_all(
|
||||
@@ -316,22 +323,21 @@ class MyEnvProcessorStep(ObservationProcessorStep):
|
||||
return processed
|
||||
```
|
||||
|
||||
### 2. Update Your `EnvConfig` Subclass
|
||||
### 2. Update the Factory
|
||||
|
||||
```python
|
||||
# In src/lerobot/envs/configs.py
|
||||
@EnvConfig.register_subclass("myenv")
|
||||
@dataclass
|
||||
class MyEnvConfig(EnvConfig):
|
||||
# ... task/features/gym kwargs ...
|
||||
# In src/lerobot/envs/factory.py
|
||||
|
||||
def get_env_processors(self):
|
||||
from lerobot.processor.pipeline import PolicyProcessorPipeline
|
||||
def make_env_pre_post_processors(env_cfg: EnvConfig):
|
||||
if isinstance(env_cfg, LiberoEnv) or "libero" in env_cfg.type:
|
||||
preprocessor = PolicyProcessorPipeline(steps=[LiberoProcessorStep()])
|
||||
elif isinstance(env_cfg, MyEnvConfig) or "myenv" in env_cfg.type:
|
||||
preprocessor = PolicyProcessorPipeline(steps=[MyEnvProcessorStep()])
|
||||
else:
|
||||
preprocessor = PolicyProcessorPipeline(steps=[])
|
||||
|
||||
return (
|
||||
PolicyProcessorPipeline(steps=[MyEnvProcessorStep()]),
|
||||
PolicyProcessorPipeline(steps=[]),
|
||||
)
|
||||
postprocessor = PolicyProcessorPipeline(steps=[])
|
||||
return preprocessor, postprocessor
|
||||
```
|
||||
|
||||
### 3. Use in Evaluation
|
||||
|
||||
+1
-1
@@ -25,7 +25,7 @@ discord = "https://discord.gg/s3KuuzsPFb"
|
||||
|
||||
[project]
|
||||
name = "lerobot"
|
||||
version = "0.5.2"
|
||||
version = "0.5.1"
|
||||
description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
|
||||
dynamic = ["readme"]
|
||||
license = { text = "Apache-2.0" }
|
||||
|
||||
@@ -151,11 +151,9 @@ class LeRobotDataset(torch.utils.data.Dataset):
|
||||
``$HF_LEROBOT_HOME/hub``.
|
||||
episodes (list[int] | None, optional): If specified, this will only load episodes specified by
|
||||
their episode_index in this list. Defaults to None.
|
||||
image_transforms (Callable | None, optional):
|
||||
Transform applied to visual modalities inside `__getitem__` after image decoding / tensor
|
||||
conversion. This works for both image-backed and video-backed observations and can later be
|
||||
updated with `set_image_transforms()` or cleared with `clear_image_transforms()`.
|
||||
Defaults to None.
|
||||
image_transforms (Callable | None, optional): You can pass standard v2 image transforms from
|
||||
torchvision.transforms.v2 here which will be applied to visual modalities (whether they come
|
||||
from videos or images). Defaults to None.
|
||||
delta_timestamps (dict[list[float]] | None, optional): _description_. Defaults to None.
|
||||
tolerance_s (float, optional): Tolerance in seconds used to ensure data timestamps are actually in
|
||||
sync with the fps value. It is used at the init of the dataset to make sure that each
|
||||
@@ -194,8 +192,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
|
||||
super().__init__()
|
||||
self.repo_id = repo_id
|
||||
self._requested_root = Path(root) if root else None
|
||||
self.reader = None
|
||||
self.set_image_transforms(image_transforms)
|
||||
self.image_transforms = image_transforms
|
||||
self.delta_timestamps = delta_timestamps
|
||||
self.episodes = episodes
|
||||
self.tolerance_s = tolerance_s
|
||||
@@ -478,18 +475,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
|
||||
f"}})"
|
||||
)
|
||||
|
||||
def set_image_transforms(self, image_transforms: Callable | None) -> None:
|
||||
"""Replace the transform applied to visual observations."""
|
||||
if image_transforms is not None and not callable(image_transforms):
|
||||
raise TypeError("image_transforms must be callable or None.")
|
||||
self.image_transforms = image_transforms
|
||||
if self.reader is not None:
|
||||
self.reader._image_transforms = image_transforms
|
||||
|
||||
def clear_image_transforms(self) -> None:
|
||||
"""Remove the transform applied to visual observations."""
|
||||
self.set_image_transforms(None)
|
||||
|
||||
# ── Hub methods (stay on facade) ──────────────────────────────────
|
||||
|
||||
def push_to_hub(
|
||||
|
||||
@@ -89,24 +89,12 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
|
||||
)
|
||||
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])
|
||||
self.set_image_transforms(image_transforms)
|
||||
|
||||
def set_image_transforms(self, image_transforms: Callable | None) -> None:
|
||||
"""Replace the transform for this dataset and its children."""
|
||||
if image_transforms is not None and not callable(image_transforms):
|
||||
raise TypeError("image_transforms must be callable or None.")
|
||||
self.image_transforms = image_transforms
|
||||
for dataset in getattr(self, "_datasets", []):
|
||||
dataset.set_image_transforms(self.image_transforms)
|
||||
|
||||
def clear_image_transforms(self) -> None:
|
||||
"""Remove the transform from this dataset and its children."""
|
||||
self.set_image_transforms(None)
|
||||
|
||||
@property
|
||||
def repo_id_to_index(self):
|
||||
|
||||
@@ -12,16 +12,11 @@
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import abc
|
||||
import importlib
|
||||
from dataclasses import dataclass, field, fields
|
||||
from typing import Any
|
||||
|
||||
import draccus
|
||||
import gymnasium as gym
|
||||
from gymnasium.envs.registration import registry as gym_registry
|
||||
|
||||
from lerobot.configs.types import FeatureType, PolicyFeature
|
||||
from lerobot.robots import RobotConfig
|
||||
@@ -72,49 +67,6 @@ class EnvConfig(draccus.ChoiceRegistry, abc.ABC):
|
||||
def gym_kwargs(self) -> dict:
|
||||
raise NotImplementedError()
|
||||
|
||||
def create_envs(
|
||||
self,
|
||||
n_envs: int,
|
||||
use_async_envs: bool = False,
|
||||
) -> dict[str, dict[int, gym.vector.VectorEnv]]:
|
||||
"""Create {suite: {task_id: VectorEnv}}.
|
||||
|
||||
Default: single-task env via gym.make(). Multi-task benchmarks override.
|
||||
"""
|
||||
env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv
|
||||
|
||||
if self.gym_id not in gym_registry:
|
||||
print(f"gym id '{self.gym_id}' not found, attempting to import '{self.package_name}'...")
|
||||
try:
|
||||
importlib.import_module(self.package_name)
|
||||
except ModuleNotFoundError as e:
|
||||
raise ModuleNotFoundError(
|
||||
f"Package '{self.package_name}' required for env '{self.type}' not found. "
|
||||
f"Please install it or check PYTHONPATH."
|
||||
) from e
|
||||
|
||||
if self.gym_id not in gym_registry:
|
||||
raise gym.error.NameNotFound(
|
||||
f"Environment '{self.gym_id}' not registered even after importing '{self.package_name}'."
|
||||
)
|
||||
|
||||
def _make_one():
|
||||
return gym.make(self.gym_id, disable_env_checker=self.disable_env_checker, **self.gym_kwargs)
|
||||
|
||||
try:
|
||||
from gymnasium.vector import AutoresetMode
|
||||
|
||||
vec = env_cls([_make_one for _ in range(n_envs)], autoreset_mode=AutoresetMode.SAME_STEP)
|
||||
except ImportError:
|
||||
vec = env_cls([_make_one for _ in range(n_envs)])
|
||||
return {self.type: {0: vec}}
|
||||
|
||||
def get_env_processors(self):
|
||||
"""Return (preprocessor, postprocessor) for this env. Default: identity."""
|
||||
from lerobot.processor.pipeline import PolicyProcessorPipeline
|
||||
|
||||
return PolicyProcessorPipeline(steps=[]), PolicyProcessorPipeline(steps=[])
|
||||
|
||||
|
||||
@dataclass
|
||||
class HubEnvConfig(EnvConfig):
|
||||
@@ -386,12 +338,6 @@ class LiberoEnv(EnvConfig):
|
||||
else:
|
||||
raise ValueError(f"Unsupported obs_type: {self.obs_type}")
|
||||
|
||||
if self.camera_name_mapping is not None:
|
||||
mapped_agentview = self.camera_name_mapping.get("agentview_image", "image")
|
||||
mapped_eye_in_hand = self.camera_name_mapping.get("robot0_eye_in_hand_image", "image2")
|
||||
self.features_map[LIBERO_KEY_PIXELS_AGENTVIEW] = f"{OBS_IMAGES}.{mapped_agentview}"
|
||||
self.features_map[LIBERO_KEY_PIXELS_EYE_IN_HAND] = f"{OBS_IMAGES}.{mapped_eye_in_hand}"
|
||||
|
||||
@property
|
||||
def gym_kwargs(self) -> dict:
|
||||
kwargs: dict[str, Any] = {"obs_type": self.obs_type, "render_mode": self.render_mode}
|
||||
@@ -399,33 +345,6 @@ class LiberoEnv(EnvConfig):
|
||||
kwargs["task_ids"] = self.task_ids
|
||||
return kwargs
|
||||
|
||||
def create_envs(self, n_envs: int, use_async_envs: bool = False):
|
||||
from lerobot.envs.libero import create_libero_envs
|
||||
|
||||
if self.task is None:
|
||||
raise ValueError("LiberoEnv requires a task to be specified")
|
||||
env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv
|
||||
return create_libero_envs(
|
||||
task=self.task,
|
||||
n_envs=n_envs,
|
||||
camera_name=self.camera_name,
|
||||
init_states=self.init_states,
|
||||
gym_kwargs=self.gym_kwargs,
|
||||
env_cls=env_cls,
|
||||
control_mode=self.control_mode,
|
||||
episode_length=self.episode_length,
|
||||
camera_name_mapping=self.camera_name_mapping,
|
||||
)
|
||||
|
||||
def get_env_processors(self):
|
||||
from lerobot.processor.env_processor import LiberoProcessorStep
|
||||
from lerobot.processor.pipeline import PolicyProcessorPipeline
|
||||
|
||||
return (
|
||||
PolicyProcessorPipeline(steps=[LiberoProcessorStep()]),
|
||||
PolicyProcessorPipeline(steps=[]),
|
||||
)
|
||||
|
||||
|
||||
@EnvConfig.register_subclass("metaworld")
|
||||
@dataclass
|
||||
@@ -468,19 +387,6 @@ class MetaworldEnv(EnvConfig):
|
||||
"render_mode": self.render_mode,
|
||||
}
|
||||
|
||||
def create_envs(self, n_envs: int, use_async_envs: bool = False):
|
||||
from lerobot.envs.metaworld import create_metaworld_envs
|
||||
|
||||
if self.task is None:
|
||||
raise ValueError("MetaWorld requires a task to be specified")
|
||||
env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv
|
||||
return create_metaworld_envs(
|
||||
task=self.task,
|
||||
n_envs=n_envs,
|
||||
gym_kwargs=self.gym_kwargs,
|
||||
env_cls=env_cls,
|
||||
)
|
||||
|
||||
|
||||
@EnvConfig.register_subclass("isaaclab_arena")
|
||||
@dataclass
|
||||
@@ -548,18 +454,3 @@ class IsaaclabArenaEnv(HubEnvConfig):
|
||||
@property
|
||||
def gym_kwargs(self) -> dict:
|
||||
return {}
|
||||
|
||||
def get_env_processors(self):
|
||||
from lerobot.processor.env_processor import IsaaclabArenaProcessorStep
|
||||
from lerobot.processor.pipeline import PolicyProcessorPipeline
|
||||
|
||||
state_keys = tuple(k.strip() for k in (self.state_keys or "").split(",") if k.strip())
|
||||
camera_keys = tuple(k.strip() for k in (self.camera_keys or "").split(",") if k.strip())
|
||||
if not state_keys and not camera_keys:
|
||||
raise ValueError("At least one of state_keys or camera_keys must be specified.")
|
||||
return (
|
||||
PolicyProcessorPipeline(
|
||||
steps=[IsaaclabArenaProcessorStep(state_keys=state_keys, camera_keys=camera_keys)]
|
||||
),
|
||||
PolicyProcessorPipeline(steps=[]),
|
||||
)
|
||||
|
||||
+117
-20
@@ -13,46 +13,90 @@
|
||||
# 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 __future__ import annotations
|
||||
|
||||
import importlib
|
||||
from typing import Any
|
||||
|
||||
import gymnasium as gym
|
||||
from gymnasium.envs.registration import registry as gym_registry
|
||||
|
||||
from lerobot.envs.configs import EnvConfig, HubEnvConfig
|
||||
from lerobot.configs.policies import PreTrainedConfig
|
||||
from lerobot.envs.configs import AlohaEnv, EnvConfig, HubEnvConfig, IsaaclabArenaEnv, LiberoEnv, PushtEnv
|
||||
from lerobot.envs.utils import _call_make_env, _download_hub_file, _import_hub_module, _normalize_hub_result
|
||||
from lerobot.policies.xvla.configuration_xvla import XVLAConfig
|
||||
from lerobot.processor import ProcessorStep
|
||||
from lerobot.processor.env_processor import IsaaclabArenaProcessorStep, LiberoProcessorStep
|
||||
from lerobot.processor.pipeline import PolicyProcessorPipeline
|
||||
|
||||
|
||||
def make_env_config(env_type: str, **kwargs) -> EnvConfig:
|
||||
try:
|
||||
cls = EnvConfig.get_choice_class(env_type)
|
||||
except KeyError as err:
|
||||
raise ValueError(
|
||||
f"Environment type '{env_type}' is not registered. "
|
||||
f"Available: {list(EnvConfig.get_known_choices().keys())}"
|
||||
) from err
|
||||
return cls(**kwargs)
|
||||
if env_type == "aloha":
|
||||
return AlohaEnv(**kwargs)
|
||||
elif env_type == "pusht":
|
||||
return PushtEnv(**kwargs)
|
||||
elif env_type == "libero":
|
||||
return LiberoEnv(**kwargs)
|
||||
else:
|
||||
raise ValueError(f"Policy type '{env_type}' is not available.")
|
||||
|
||||
|
||||
def make_env_pre_post_processors(
|
||||
env_cfg: EnvConfig,
|
||||
policy_cfg: Any,
|
||||
) -> tuple[Any, Any]:
|
||||
policy_cfg: PreTrainedConfig,
|
||||
) -> tuple[
|
||||
PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
|
||||
PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
|
||||
]:
|
||||
"""
|
||||
Create preprocessor and postprocessor pipelines for environment observations.
|
||||
|
||||
Returns a tuple of (preprocessor, postprocessor). By default, delegates to
|
||||
``env_cfg.get_env_processors()``. The XVLAConfig policy-specific override
|
||||
stays here because it depends on the *policy* config, not the env config.
|
||||
"""
|
||||
from lerobot.policies.xvla.configuration_xvla import XVLAConfig
|
||||
This function creates processor pipelines that transform raw environment
|
||||
observations and actions. By default, it returns identity processors that do nothing.
|
||||
For specific environments like LIBERO, it adds environment-specific processing steps.
|
||||
|
||||
Args:
|
||||
env_cfg: The configuration of the environment.
|
||||
|
||||
Returns:
|
||||
A tuple containing:
|
||||
- preprocessor: Pipeline that processes environment observations
|
||||
- postprocessor: Pipeline that processes environment outputs (currently identity)
|
||||
"""
|
||||
# Preprocessor and Postprocessor steps are Identity for most environments
|
||||
preprocessor_steps: list[ProcessorStep] = []
|
||||
postprocessor_steps: list[ProcessorStep] = []
|
||||
if isinstance(policy_cfg, XVLAConfig):
|
||||
from lerobot.policies.xvla.processor_xvla import make_xvla_libero_pre_post_processors
|
||||
|
||||
return make_xvla_libero_pre_post_processors()
|
||||
|
||||
return env_cfg.get_env_processors()
|
||||
# For LIBERO environments, add the LiberoProcessorStep to preprocessor
|
||||
if isinstance(env_cfg, LiberoEnv) or "libero" in env_cfg.type:
|
||||
preprocessor_steps.append(LiberoProcessorStep())
|
||||
|
||||
# For Isaaclab Arena environments, add the IsaaclabArenaProcessorStep
|
||||
if isinstance(env_cfg, IsaaclabArenaEnv) or "isaaclab_arena" in env_cfg.type:
|
||||
# Parse comma-separated keys (handle None for state-based policies)
|
||||
if env_cfg.state_keys:
|
||||
state_keys = tuple(k.strip() for k in env_cfg.state_keys.split(",") if k.strip())
|
||||
else:
|
||||
state_keys = ()
|
||||
if env_cfg.camera_keys:
|
||||
camera_keys = tuple(k.strip() for k in env_cfg.camera_keys.split(",") if k.strip())
|
||||
else:
|
||||
camera_keys = ()
|
||||
if not state_keys and not camera_keys:
|
||||
raise ValueError("At least one of state_keys or camera_keys must be specified.")
|
||||
preprocessor_steps.append(
|
||||
IsaaclabArenaProcessorStep(
|
||||
state_keys=state_keys,
|
||||
camera_keys=camera_keys,
|
||||
)
|
||||
)
|
||||
|
||||
preprocessor = PolicyProcessorPipeline(steps=preprocessor_steps)
|
||||
postprocessor = PolicyProcessorPipeline(steps=postprocessor_steps)
|
||||
|
||||
return preprocessor, postprocessor
|
||||
|
||||
|
||||
def make_env(
|
||||
@@ -119,4 +163,57 @@ def make_env(
|
||||
if n_envs < 1:
|
||||
raise ValueError("`n_envs` must be at least 1")
|
||||
|
||||
return cfg.create_envs(n_envs=n_envs, use_async_envs=use_async_envs)
|
||||
env_cls = gym.vector.AsyncVectorEnv if use_async_envs else gym.vector.SyncVectorEnv
|
||||
|
||||
if "libero" in cfg.type:
|
||||
from lerobot.envs.libero import create_libero_envs
|
||||
|
||||
if cfg.task is None:
|
||||
raise ValueError("LiberoEnv requires a task to be specified")
|
||||
|
||||
return create_libero_envs(
|
||||
task=cfg.task,
|
||||
n_envs=n_envs,
|
||||
camera_name=cfg.camera_name,
|
||||
init_states=cfg.init_states,
|
||||
gym_kwargs=cfg.gym_kwargs,
|
||||
env_cls=env_cls,
|
||||
control_mode=cfg.control_mode,
|
||||
episode_length=cfg.episode_length,
|
||||
)
|
||||
elif "metaworld" in cfg.type:
|
||||
from lerobot.envs.metaworld import create_metaworld_envs
|
||||
|
||||
if cfg.task is None:
|
||||
raise ValueError("MetaWorld requires a task to be specified")
|
||||
|
||||
return create_metaworld_envs(
|
||||
task=cfg.task,
|
||||
n_envs=n_envs,
|
||||
gym_kwargs=cfg.gym_kwargs,
|
||||
env_cls=env_cls,
|
||||
)
|
||||
|
||||
if cfg.gym_id not in gym_registry:
|
||||
print(f"gym id '{cfg.gym_id}' not found, attempting to import '{cfg.package_name}'...")
|
||||
try:
|
||||
importlib.import_module(cfg.package_name)
|
||||
except ModuleNotFoundError as e:
|
||||
raise ModuleNotFoundError(
|
||||
f"Package '{cfg.package_name}' required for env '{cfg.type}' not found. "
|
||||
f"Please install it or check PYTHONPATH."
|
||||
) from e
|
||||
|
||||
if cfg.gym_id not in gym_registry:
|
||||
raise gym.error.NameNotFound(
|
||||
f"Environment '{cfg.gym_id}' not registered even after importing '{cfg.package_name}'."
|
||||
)
|
||||
|
||||
def _make_one():
|
||||
return gym.make(cfg.gym_id, disable_env_checker=cfg.disable_env_checker, **(cfg.gym_kwargs or {}))
|
||||
|
||||
vec = env_cls([_make_one for _ in range(n_envs)], autoreset_mode=gym.vector.AutoresetMode.SAME_STEP)
|
||||
|
||||
# normalize to {suite: {task_id: vec_env}} for consistency
|
||||
suite_name = cfg.type # e.g., "pusht", "aloha"
|
||||
return {suite_name: {0: vec}}
|
||||
|
||||
@@ -223,8 +223,7 @@ class LiberoEnv(gym.Env):
|
||||
|
||||
def render(self):
|
||||
raw_obs = self._env.env._get_observations()
|
||||
pixels = self._format_raw_obs(raw_obs)["pixels"]
|
||||
image = next(iter(pixels.values()))
|
||||
image = self._format_raw_obs(raw_obs)["pixels"]["image"]
|
||||
image = image[::-1, ::-1] # flip both H and W for visualization
|
||||
return image
|
||||
|
||||
@@ -340,6 +339,12 @@ class LiberoEnv(gym.Env):
|
||||
)
|
||||
observation = self._format_raw_obs(raw_obs)
|
||||
if terminated:
|
||||
info["final_info"] = {
|
||||
"task": self.task,
|
||||
"task_id": self.task_id,
|
||||
"done": bool(done),
|
||||
"is_success": bool(is_success),
|
||||
}
|
||||
self.reset()
|
||||
truncated = False
|
||||
return observation, reward, terminated, truncated, info
|
||||
@@ -359,7 +364,6 @@ def _make_env_fns(
|
||||
init_states: bool,
|
||||
gym_kwargs: Mapping[str, Any],
|
||||
control_mode: str,
|
||||
camera_name_mapping: dict[str, str] | None = None,
|
||||
) -> list[Callable[[], LiberoEnv]]:
|
||||
"""Build n_envs factory callables for a single (suite, task_id)."""
|
||||
|
||||
@@ -375,7 +379,6 @@ def _make_env_fns(
|
||||
episode_index=episode_index,
|
||||
n_envs=n_envs,
|
||||
control_mode=control_mode,
|
||||
camera_name_mapping=camera_name_mapping,
|
||||
**local_kwargs,
|
||||
)
|
||||
|
||||
@@ -397,7 +400,6 @@ def create_libero_envs(
|
||||
env_cls: Callable[[Sequence[Callable[[], Any]]], Any] | None = None,
|
||||
control_mode: str = "relative",
|
||||
episode_length: int | None = None,
|
||||
camera_name_mapping: dict[str, str] | None = None,
|
||||
) -> dict[str, dict[int, Any]]:
|
||||
"""
|
||||
Create vectorized LIBERO environments with a consistent return shape.
|
||||
@@ -447,7 +449,6 @@ def create_libero_envs(
|
||||
init_states=init_states,
|
||||
gym_kwargs=gym_kwargs,
|
||||
control_mode=control_mode,
|
||||
camera_name_mapping=camera_name_mapping,
|
||||
)
|
||||
out[suite_name][tid] = env_cls(fns)
|
||||
print(f"Built vec env | suite={suite_name} | task_id={tid} | n_envs={n_envs}")
|
||||
|
||||
@@ -201,11 +201,6 @@ def rollout(
|
||||
"You're likely using an older version of gymnasium (< 1.0). Please upgrade."
|
||||
)
|
||||
successes = final_info["is_success"].tolist()
|
||||
elif "is_success" in info:
|
||||
is_success = info["is_success"]
|
||||
successes = (
|
||||
is_success.tolist() if hasattr(is_success, "tolist") else [bool(is_success)] * env.num_envs
|
||||
)
|
||||
else:
|
||||
successes = [False] * env.num_envs
|
||||
|
||||
|
||||
@@ -421,7 +421,6 @@ def record_loop(
|
||||
act_processed_policy: RobotAction = make_robot_action(action_values, dataset.features)
|
||||
# Applies a pipeline to the action, default is IdentityProcessor
|
||||
robot_action_to_send = robot_action_processor((act_processed_policy, obs))
|
||||
action_values = robot_action_to_send
|
||||
|
||||
elif policy is None and isinstance(teleop, Teleoperator):
|
||||
act = teleop.get_action()
|
||||
|
||||
@@ -24,7 +24,6 @@ import torch
|
||||
from huggingface_hub import HfApi
|
||||
from PIL import Image
|
||||
from safetensors.torch import load_file
|
||||
from torchvision.transforms import v2
|
||||
|
||||
import lerobot
|
||||
from lerobot.configs.default import DatasetConfig
|
||||
@@ -35,7 +34,6 @@ from lerobot.datasets.image_writer import image_array_to_pil_image
|
||||
from lerobot.datasets.io_utils import hf_transform_to_torch
|
||||
from lerobot.datasets.lerobot_dataset import LeRobotDataset
|
||||
from lerobot.datasets.multi_dataset import MultiLeRobotDataset
|
||||
from lerobot.datasets.transforms import ImageTransforms, ImageTransformsConfig
|
||||
from lerobot.datasets.utils import (
|
||||
DEFAULT_CHUNK_SIZE,
|
||||
DEFAULT_DATA_FILE_SIZE_IN_MB,
|
||||
@@ -357,62 +355,6 @@ def test_add_frame_image_pil(image_dataset):
|
||||
assert dataset[0]["image"].shape == torch.Size(DUMMY_CHW)
|
||||
|
||||
|
||||
def test_set_image_transforms_applies_transparently(image_dataset):
|
||||
dataset = image_dataset
|
||||
dataset.add_frame({"image": np.random.rand(*DUMMY_CHW), "task": "Dummy task"})
|
||||
dataset.save_episode()
|
||||
dataset.finalize()
|
||||
|
||||
dataset.set_image_transforms(v2.Resize((224, 224)))
|
||||
assert dataset[0]["image"].shape == torch.Size((3, 224, 224))
|
||||
|
||||
dataset.set_image_transforms(v2.Resize((128, 128)))
|
||||
assert dataset[0]["image"].shape == torch.Size((3, 128, 128))
|
||||
|
||||
dataset.clear_image_transforms()
|
||||
assert dataset[0]["image"].shape == torch.Size(DUMMY_CHW)
|
||||
|
||||
|
||||
def test_set_image_transforms_supports_lerobot_image_transforms(image_dataset):
|
||||
dataset = image_dataset
|
||||
dataset.add_frame({"image": np.random.rand(*DUMMY_CHW), "task": "Dummy task"})
|
||||
dataset.save_episode()
|
||||
dataset.finalize()
|
||||
|
||||
image_transforms = ImageTransforms(ImageTransformsConfig(enable=False))
|
||||
dataset.set_image_transforms(image_transforms)
|
||||
|
||||
assert dataset.image_transforms is image_transforms
|
||||
assert dataset[0]["image"].shape == torch.Size(DUMMY_CHW)
|
||||
|
||||
|
||||
def test_set_image_transforms_supports_loaded_dataset(tmp_path, lerobot_dataset_factory):
|
||||
dataset = lerobot_dataset_factory(root=tmp_path / "test", use_videos=False)
|
||||
dataset.set_image_transforms(v2.Compose([v2.Resize((224, 224)), v2.Resize((112, 112))]))
|
||||
|
||||
camera_key = dataset.meta.camera_keys[0]
|
||||
assert dataset[0][camera_key].shape == torch.Size((3, 112, 112))
|
||||
|
||||
|
||||
def test_multilerobot_dataset_set_image_transforms_propagates(tmp_path, lerobot_dataset_factory):
|
||||
root = tmp_path / "multi"
|
||||
repo_ids = ["lerobot/test_multi_a", "lerobot/test_multi_b"]
|
||||
|
||||
for repo_id in repo_ids:
|
||||
lerobot_dataset_factory(root=root / repo_id, repo_id=repo_id, use_videos=False)
|
||||
|
||||
dataset = MultiLeRobotDataset(repo_ids, root=root, download_videos=False)
|
||||
dataset.set_image_transforms(v2.Resize((96, 96)))
|
||||
|
||||
camera_key = dataset.camera_keys[0]
|
||||
assert dataset[0][camera_key].shape == torch.Size((3, 96, 96))
|
||||
assert all(child.image_transforms is dataset.image_transforms for child in dataset._datasets)
|
||||
|
||||
dataset.clear_image_transforms()
|
||||
assert dataset.image_transforms is None
|
||||
assert all(child.image_transforms is None for child in dataset._datasets)
|
||||
|
||||
|
||||
def test_image_array_to_pil_image_wrong_range_float_0_255():
|
||||
image = np.random.rand(*DUMMY_HWC) * 255
|
||||
with pytest.raises(ValueError):
|
||||
|
||||
@@ -1,143 +0,0 @@
|
||||
"""Tests for the benchmark dispatch refactor (create_envs / get_env_processors on EnvConfig)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
import gymnasium as gym
|
||||
import pytest
|
||||
from gymnasium.envs.registration import register, registry as gym_registry
|
||||
|
||||
from lerobot.configs.types import PolicyFeature
|
||||
from lerobot.envs.configs import EnvConfig
|
||||
from lerobot.envs.factory import make_env, make_env_config, make_env_pre_post_processors
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def test_registry_all_types():
|
||||
"""make_env_config should resolve every registered EnvConfig subclass via the registry."""
|
||||
known = list(EnvConfig.get_known_choices().keys())
|
||||
assert len(known) >= 6
|
||||
for t in known:
|
||||
cfg = make_env_config(t)
|
||||
if not isinstance(cfg, EnvConfig):
|
||||
continue
|
||||
assert cfg.type == t
|
||||
|
||||
|
||||
def test_unknown_type():
|
||||
with pytest.raises(ValueError, match="not registered"):
|
||||
make_env_config("nonexistent")
|
||||
|
||||
|
||||
def test_identity_processors():
|
||||
"""Base class get_env_processors() returns identity pipelines."""
|
||||
cfg = make_env_config("aloha")
|
||||
pre, post = cfg.get_env_processors()
|
||||
assert len(pre.steps) == 0 and len(post.steps) == 0
|
||||
|
||||
|
||||
def test_delegation():
|
||||
"""make_env() should call cfg.create_envs(), not use if/elif dispatch."""
|
||||
sentinel = {"delegated": {0: "marker"}}
|
||||
fake = type(
|
||||
"Fake",
|
||||
(),
|
||||
{
|
||||
"hub_path": None,
|
||||
"create_envs": lambda self, n_envs, use_async_envs=False: sentinel,
|
||||
},
|
||||
)()
|
||||
result = make_env(fake, n_envs=1)
|
||||
assert result is sentinel
|
||||
|
||||
|
||||
def test_processors_delegation():
|
||||
"""make_env_pre_post_processors delegates to cfg.get_env_processors()."""
|
||||
cfg = make_env_config("aloha")
|
||||
pre, post = make_env_pre_post_processors(cfg, policy_cfg=None)
|
||||
assert len(pre.steps) == 0
|
||||
|
||||
|
||||
def test_base_create_envs():
|
||||
"""Base class create_envs() should build a single-task VectorEnv via gym.make()."""
|
||||
gym_id = "_dispatch_test/CartPole-v99"
|
||||
if gym_id not in gym_registry:
|
||||
register(id=gym_id, entry_point="gymnasium.envs.classic_control:CartPoleEnv")
|
||||
|
||||
@EnvConfig.register_subclass("_dispatch_base_test")
|
||||
@dataclass
|
||||
class _Env(EnvConfig):
|
||||
task: str = "CartPole-v99"
|
||||
fps: int = 10
|
||||
features: dict[str, PolicyFeature] = field(default_factory=dict)
|
||||
|
||||
@property
|
||||
def package_name(self):
|
||||
return "_dispatch_test"
|
||||
|
||||
@property
|
||||
def gym_id(self):
|
||||
return gym_id
|
||||
|
||||
@property
|
||||
def gym_kwargs(self):
|
||||
return {}
|
||||
|
||||
try:
|
||||
envs = _Env().create_envs(n_envs=2)
|
||||
assert "_dispatch_base_test" in envs
|
||||
env = envs["_dispatch_base_test"][0]
|
||||
assert isinstance(env, gym.vector.SyncVectorEnv)
|
||||
assert env.num_envs == 2
|
||||
env.close()
|
||||
finally:
|
||||
if gym_id in gym_registry:
|
||||
del gym_registry[gym_id]
|
||||
|
||||
|
||||
def test_custom_create_envs_override():
|
||||
"""A custom EnvConfig subclass can override create_envs()."""
|
||||
mock_vec = gym.vector.SyncVectorEnv([lambda: gym.make("CartPole-v1")])
|
||||
|
||||
@EnvConfig.register_subclass("_dispatch_custom_test")
|
||||
@dataclass
|
||||
class _Env(EnvConfig):
|
||||
task: str = "x"
|
||||
features: dict[str, PolicyFeature] = field(default_factory=dict)
|
||||
|
||||
@property
|
||||
def gym_kwargs(self):
|
||||
return {}
|
||||
|
||||
def create_envs(self, n_envs, use_async_envs=False):
|
||||
return {"custom_suite": {0: mock_vec}}
|
||||
|
||||
try:
|
||||
result = make_env(_Env(), n_envs=1)
|
||||
assert "custom_suite" in result
|
||||
finally:
|
||||
mock_vec.close()
|
||||
|
||||
|
||||
def test_custom_get_env_processors_override():
|
||||
"""A custom EnvConfig subclass can override get_env_processors()."""
|
||||
from lerobot.processor.pipeline import DataProcessorPipeline
|
||||
|
||||
@EnvConfig.register_subclass("_dispatch_proc_test")
|
||||
@dataclass
|
||||
class _Env(EnvConfig):
|
||||
task: str = "x"
|
||||
features: dict[str, PolicyFeature] = field(default_factory=dict)
|
||||
|
||||
@property
|
||||
def gym_kwargs(self):
|
||||
return {}
|
||||
|
||||
def get_env_processors(self):
|
||||
return DataProcessorPipeline(steps=[]), DataProcessorPipeline(steps=[])
|
||||
|
||||
pre, post = _Env().get_env_processors()
|
||||
assert isinstance(pre, DataProcessorPipeline)
|
||||
@@ -832,10 +832,10 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "cuda-pathfinder"
|
||||
version = "1.5.2"
|
||||
version = "1.5.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/f2/f9/1b9b60a30fc463c14cdea7a77228131a0ccc89572e8df9cb86c9648271ab/cuda_pathfinder-1.5.2-py3-none-any.whl", hash = "sha256:0c5f160a7756c5b072723cbbd6d861e38917ef956c68150b02f0b6e9271c71fa", size = 49988, upload-time = "2026-04-06T23:01:05.17Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/c4/74/8c66861b873d8eed51fde56d3091baa4906a56f0d4390cae991f2d41dda5/cuda_pathfinder-1.5.1-py3-none-any.whl", hash = "sha256:b3718097fb57cf9e8a904dd072d806f2c9a27627e35c020b06ab9454bcec08c0", size = 49861, upload-time = "2026-04-03T16:41:22.203Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -1828,7 +1828,7 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "huggingface-hub"
|
||||
version = "1.9.1"
|
||||
version = "1.9.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "filelock" },
|
||||
@@ -1841,9 +1841,9 @@ dependencies = [
|
||||
{ name = "typer" },
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/44/40/68d9b286b125d9318ae95c8f8b206e8672e7244b0eea61ebb4a88037638c/huggingface_hub-1.9.1.tar.gz", hash = "sha256:442af372207cc24dcb089caf507fcd7dbc1217c11d6059a06f6b90afe64e8bd2", size = 750355, upload-time = "2026-04-07T13:47:59.167Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/88/bb/62c7aa86f63a05e2f9b96642fdef9b94526a23979820b09f5455deff4983/huggingface_hub-1.9.0.tar.gz", hash = "sha256:0ea5be7a56135c91797cae6ad726e38eaeb6eb4b77cefff5c9d38ba0ecf874f7", size = 750326, upload-time = "2026-04-03T08:35:55.888Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/3d/af/10a89c54937dccf6c10792770f362d96dd67aedfde108e6e1fd7a0836789/huggingface_hub-1.9.1-py3-none-any.whl", hash = "sha256:8dae771b969b318203727a6c6c5209d25e661f6f0dd010fc09cc4a12cf81c657", size = 637356, upload-time = "2026-04-07T13:47:57.239Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/73/37/0d15d16150e1829f3e90962c99f28257f6de9e526a680b4c6f5acdb54fd2/huggingface_hub-1.9.0-py3-none-any.whl", hash = "sha256:2999328c058d39fd19ab748dd09bd4da2fbaa4f4c1ddea823eab103051e14a1f", size = 637355, upload-time = "2026-04-03T08:35:53.897Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -2184,7 +2184,7 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "lerobot"
|
||||
version = "0.5.2"
|
||||
version = "0.5.1"
|
||||
source = { editable = "." }
|
||||
dependencies = [
|
||||
{ name = "accelerate" },
|
||||
@@ -5561,15 +5561,15 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "uvicorn"
|
||||
version = "0.44.0"
|
||||
version = "0.43.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "click" },
|
||||
{ name = "h11" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/5e/da/6eee1ff8b6cbeed47eeb5229749168e81eb4b7b999a1a15a7176e51410c9/uvicorn-0.44.0.tar.gz", hash = "sha256:6c942071b68f07e178264b9152f1f16dfac5da85880c4ce06366a96d70d4f31e", size = 86947, upload-time = "2026-04-06T09:23:22.826Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/62/f2/368268300fb8af33743508d738ef7bb4d56afdb46c6d9c0fa3dd515df171/uvicorn-0.43.0.tar.gz", hash = "sha256:ab1652d2fb23abf124f36ccc399828558880def222c3cb3d98d24021520dc6e8", size = 85686, upload-time = "2026-04-03T18:37:48.984Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/b7/23/a5bbd9600dd607411fa644c06ff4951bec3a4d82c4b852374024359c19c0/uvicorn-0.44.0-py3-none-any.whl", hash = "sha256:ce937c99a2cc70279556967274414c087888e8cec9f9c94644dfca11bd3ced89", size = 69425, upload-time = "2026-04-06T09:23:21.524Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/55/df/0cf5b0c451602748fdc7a702d4667f6e209bf96aa6e3160d754234445f2a/uvicorn-0.43.0-py3-none-any.whl", hash = "sha256:46fac64f487fd968cd999e5e49efbbe64bd231b5bd8b4a0b482a23ebce499620", size = 68591, upload-time = "2026-04-03T18:37:47.64Z" },
|
||||
]
|
||||
|
||||
[package.optional-dependencies]
|
||||
|
||||
Reference in New Issue
Block a user