Merge remote-tracking branch 'upstream/main' into add_drop_last_keyframes
This commit is contained in:
Alexander Soare
commit
946d191919
|
|
@ -1,2 +1,6 @@
|
||||||
*.memmap filter=lfs diff=lfs merge=lfs -text
|
*.memmap filter=lfs diff=lfs merge=lfs -text
|
||||||
*.stl filter=lfs diff=lfs merge=lfs -text
|
*.stl filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.mp4 filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.arrow filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.json filter=lfs diff=lfs merge=lfs -text
|
||||||
|
|
|
||||||
|
|
@ -10,7 +10,6 @@ on:
|
||||||
|
|
||||||
env:
|
env:
|
||||||
PYTHON_VERSION: "3.10"
|
PYTHON_VERSION: "3.10"
|
||||||
# CI_SLACK_CHANNEL: ${{ secrets.CI_DOCKER_CHANNEL }}
|
|
||||||
|
|
||||||
jobs:
|
jobs:
|
||||||
latest-cpu:
|
latest-cpu:
|
||||||
|
|
@ -51,30 +50,6 @@ jobs:
|
||||||
tags: huggingface/lerobot-cpu
|
tags: huggingface/lerobot-cpu
|
||||||
build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}
|
build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}
|
||||||
|
|
||||||
# - name: Post to a Slack channel
|
|
||||||
# id: slack
|
|
||||||
# #uses: slackapi/slack-github-action@v1.25.0
|
|
||||||
# uses: slackapi/slack-github-action@6c661ce58804a1a20f6dc5fbee7f0381b469e001
|
|
||||||
# with:
|
|
||||||
# # Slack channel id, channel name, or user id to post message.
|
|
||||||
# # See also: https://api.slack.com/methods/chat.postMessage#channels
|
|
||||||
# channel-id: ${{ env.CI_SLACK_CHANNEL }}
|
|
||||||
# # For posting a rich message using Block Kit
|
|
||||||
# payload: |
|
|
||||||
# {
|
|
||||||
# "text": "lerobot-cpu Docker Image build result: ${{ job.status }}\n${{ github.event.pull_request.html_url || github.event.head_commit.url }}",
|
|
||||||
# "blocks": [
|
|
||||||
# {
|
|
||||||
# "type": "section",
|
|
||||||
# "text": {
|
|
||||||
# "type": "mrkdwn",
|
|
||||||
# "text": "lerobot-cpu Docker Image build result: ${{ job.status }}\n${{ github.event.pull_request.html_url || github.event.head_commit.url }}"
|
|
||||||
# }
|
|
||||||
# }
|
|
||||||
# ]
|
|
||||||
# }
|
|
||||||
# env:
|
|
||||||
# SLACK_BOT_TOKEN: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
|
|
||||||
|
|
||||||
latest-cuda:
|
latest-cuda:
|
||||||
name: GPU
|
name: GPU
|
||||||
|
|
@ -113,27 +88,40 @@ jobs:
|
||||||
tags: huggingface/lerobot-gpu
|
tags: huggingface/lerobot-gpu
|
||||||
build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}
|
build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}
|
||||||
|
|
||||||
# - name: Post to a Slack channel
|
|
||||||
# id: slack
|
latest-cuda-dev:
|
||||||
# #uses: slackapi/slack-github-action@v1.25.0
|
name: GPU Dev
|
||||||
# uses: slackapi/slack-github-action@6c661ce58804a1a20f6dc5fbee7f0381b469e001
|
runs-on: ubuntu-latest
|
||||||
# with:
|
steps:
|
||||||
# # Slack channel id, channel name, or user id to post message.
|
- name: Cleanup disk
|
||||||
# # See also: https://api.slack.com/methods/chat.postMessage#channels
|
run: |
|
||||||
# channel-id: ${{ env.CI_SLACK_CHANNEL }}
|
sudo df -h
|
||||||
# # For posting a rich message using Block Kit
|
# sudo ls -l /usr/local/lib/
|
||||||
# payload: |
|
# sudo ls -l /usr/share/
|
||||||
# {
|
sudo du -sh /usr/local/lib/
|
||||||
# "text": "lerobot-gpu Docker Image build result: ${{ job.status }}\n${{ github.event.pull_request.html_url || github.event.head_commit.url }}",
|
sudo du -sh /usr/share/
|
||||||
# "blocks": [
|
sudo rm -rf /usr/local/lib/android
|
||||||
# {
|
sudo rm -rf /usr/share/dotnet
|
||||||
# "type": "section",
|
sudo du -sh /usr/local/lib/
|
||||||
# "text": {
|
sudo du -sh /usr/share/
|
||||||
# "type": "mrkdwn",
|
sudo df -h
|
||||||
# "text": "lerobot-gpu Docker Image build result: ${{ job.status }}\n${{ github.event.pull_request.html_url || github.event.head_commit.url }}"
|
- name: Set up Docker Buildx
|
||||||
# }
|
uses: docker/setup-buildx-action@v3
|
||||||
# }
|
|
||||||
# ]
|
- name: Check out code
|
||||||
# }
|
uses: actions/checkout@v4
|
||||||
# env:
|
|
||||||
# SLACK_BOT_TOKEN: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
|
- name: Login to DockerHub
|
||||||
|
uses: docker/login-action@v3
|
||||||
|
with:
|
||||||
|
username: ${{ secrets.DOCKERHUB_USERNAME }}
|
||||||
|
password: ${{ secrets.DOCKERHUB_PASSWORD }}
|
||||||
|
|
||||||
|
- name: Build and Push GPU dev
|
||||||
|
uses: docker/build-push-action@v5
|
||||||
|
with:
|
||||||
|
context: .
|
||||||
|
file: ./docker/lerobot-gpu-dev/Dockerfile
|
||||||
|
push: true
|
||||||
|
tags: huggingface/lerobot-gpu:dev
|
||||||
|
build-args: PYTHON_VERSION=${{ env.PYTHON_VERSION }}
|
||||||
|
|
|
||||||
|
|
@ -29,6 +29,8 @@ jobs:
|
||||||
MUJOCO_GL: egl
|
MUJOCO_GL: egl
|
||||||
steps:
|
steps:
|
||||||
- uses: actions/checkout@v4
|
- uses: actions/checkout@v4
|
||||||
|
with:
|
||||||
|
lfs: true # Ensure LFS files are pulled
|
||||||
|
|
||||||
- name: Install EGL
|
- name: Install EGL
|
||||||
run: sudo apt-get update && sudo apt-get install -y libegl1-mesa-dev
|
run: sudo apt-get update && sudo apt-get install -y libegl1-mesa-dev
|
||||||
|
|
@ -65,6 +67,8 @@ jobs:
|
||||||
MUJOCO_GL: egl
|
MUJOCO_GL: egl
|
||||||
steps:
|
steps:
|
||||||
- uses: actions/checkout@v4
|
- uses: actions/checkout@v4
|
||||||
|
with:
|
||||||
|
lfs: true # Ensure LFS files are pulled
|
||||||
|
|
||||||
- name: Install poetry
|
- name: Install poetry
|
||||||
run: |
|
run: |
|
||||||
|
|
@ -97,6 +101,8 @@ jobs:
|
||||||
MUJOCO_GL: egl
|
MUJOCO_GL: egl
|
||||||
steps:
|
steps:
|
||||||
- uses: actions/checkout@v4
|
- uses: actions/checkout@v4
|
||||||
|
with:
|
||||||
|
lfs: true # Ensure LFS files are pulled
|
||||||
|
|
||||||
- name: Install EGL
|
- name: Install EGL
|
||||||
run: sudo apt-get update && sudo apt-get install -y libegl1-mesa-dev
|
run: sudo apt-get update && sudo apt-get install -y libegl1-mesa-dev
|
||||||
|
|
|
||||||
|
|
@ -2,12 +2,17 @@
|
||||||
logs
|
logs
|
||||||
tmp
|
tmp
|
||||||
wandb
|
wandb
|
||||||
|
|
||||||
|
# Data
|
||||||
data
|
data
|
||||||
outputs
|
outputs
|
||||||
.vscode
|
|
||||||
rl
|
# Apple
|
||||||
.DS_Store
|
.DS_Store
|
||||||
|
|
||||||
|
# VS Code
|
||||||
|
.vscode
|
||||||
|
|
||||||
# HPC
|
# HPC
|
||||||
nautilus/*.yaml
|
nautilus/*.yaml
|
||||||
*.key
|
*.key
|
||||||
|
|
@ -90,6 +95,7 @@ instance/
|
||||||
docs/_build/
|
docs/_build/
|
||||||
|
|
||||||
# PyBuilder
|
# PyBuilder
|
||||||
|
.pybuilder/
|
||||||
target/
|
target/
|
||||||
|
|
||||||
# Jupyter Notebook
|
# Jupyter Notebook
|
||||||
|
|
@ -102,13 +108,6 @@ ipython_config.py
|
||||||
# pyenv
|
# pyenv
|
||||||
.python-version
|
.python-version
|
||||||
|
|
||||||
# pipenv
|
|
||||||
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
|
|
||||||
# However, in case of collaboration, if having platform-specific dependencies or dependencies
|
|
||||||
# having no cross-platform support, pipenv may install dependencies that don't work, or not
|
|
||||||
# install all needed dependencies.
|
|
||||||
#Pipfile.lock
|
|
||||||
|
|
||||||
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
|
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
|
||||||
__pypackages__/
|
__pypackages__/
|
||||||
|
|
||||||
|
|
@ -119,6 +118,15 @@ celerybeat.pid
|
||||||
# SageMath parsed files
|
# SageMath parsed files
|
||||||
*.sage.py
|
*.sage.py
|
||||||
|
|
||||||
|
# Environments
|
||||||
|
.env
|
||||||
|
.venv
|
||||||
|
env/
|
||||||
|
venv/
|
||||||
|
ENV/
|
||||||
|
env.bak/
|
||||||
|
venv.bak/
|
||||||
|
|
||||||
# Spyder project settings
|
# Spyder project settings
|
||||||
.spyderproject
|
.spyderproject
|
||||||
.spyproject
|
.spyproject
|
||||||
|
|
@ -136,3 +144,9 @@ dmypy.json
|
||||||
|
|
||||||
# Pyre type checker
|
# Pyre type checker
|
||||||
.pyre/
|
.pyre/
|
||||||
|
|
||||||
|
# pytype static type analyzer
|
||||||
|
.pytype/
|
||||||
|
|
||||||
|
# Cython debug symbols
|
||||||
|
cython_debug/
|
||||||
|
|
|
||||||
|
|
@ -195,6 +195,11 @@ Follow these steps to start contributing:
|
||||||
git commit
|
git commit
|
||||||
```
|
```
|
||||||
|
|
||||||
|
Note, if you already commited some changes that have a wrong formatting, you can use:
|
||||||
|
```bash
|
||||||
|
pre-commit run --all-files
|
||||||
|
```
|
||||||
|
|
||||||
Please write [good commit messages](https://chris.beams.io/posts/git-commit/).
|
Please write [good commit messages](https://chris.beams.io/posts/git-commit/).
|
||||||
|
|
||||||
It is a good idea to sync your copy of the code with the original
|
It is a good idea to sync your copy of the code with the original
|
||||||
|
|
|
||||||
57
Makefile
57
Makefile
|
|
@ -20,15 +20,19 @@ build-gpu:
|
||||||
test-end-to-end:
|
test-end-to-end:
|
||||||
${MAKE} test-act-ete-train
|
${MAKE} test-act-ete-train
|
||||||
${MAKE} test-act-ete-eval
|
${MAKE} test-act-ete-eval
|
||||||
|
${MAKE} test-act-ete-train-amp
|
||||||
|
${MAKE} test-act-ete-eval-amp
|
||||||
${MAKE} test-diffusion-ete-train
|
${MAKE} test-diffusion-ete-train
|
||||||
${MAKE} test-diffusion-ete-eval
|
${MAKE} test-diffusion-ete-eval
|
||||||
${MAKE} test-tdmpc-ete-train
|
${MAKE} test-tdmpc-ete-train
|
||||||
${MAKE} test-tdmpc-ete-eval
|
${MAKE} test-tdmpc-ete-eval
|
||||||
${MAKE} test-default-ete-eval
|
${MAKE} test-default-ete-eval
|
||||||
|
${MAKE} test-act-pusht-tutorial
|
||||||
|
|
||||||
test-act-ete-train:
|
test-act-ete-train:
|
||||||
python lerobot/scripts/train.py \
|
python lerobot/scripts/train.py \
|
||||||
policy=act \
|
policy=act \
|
||||||
|
policy.dim_model=64 \
|
||||||
env=aloha \
|
env=aloha \
|
||||||
wandb.enable=False \
|
wandb.enable=False \
|
||||||
training.offline_steps=2 \
|
training.offline_steps=2 \
|
||||||
|
|
@ -51,9 +55,40 @@ test-act-ete-eval:
|
||||||
env.episode_length=8 \
|
env.episode_length=8 \
|
||||||
device=cpu \
|
device=cpu \
|
||||||
|
|
||||||
|
test-act-ete-train-amp:
|
||||||
|
python lerobot/scripts/train.py \
|
||||||
|
policy=act \
|
||||||
|
policy.dim_model=64 \
|
||||||
|
env=aloha \
|
||||||
|
wandb.enable=False \
|
||||||
|
training.offline_steps=2 \
|
||||||
|
training.online_steps=0 \
|
||||||
|
eval.n_episodes=1 \
|
||||||
|
eval.batch_size=1 \
|
||||||
|
device=cpu \
|
||||||
|
training.save_model=true \
|
||||||
|
training.save_freq=2 \
|
||||||
|
policy.n_action_steps=20 \
|
||||||
|
policy.chunk_size=20 \
|
||||||
|
training.batch_size=2 \
|
||||||
|
hydra.run.dir=tests/outputs/act/ \
|
||||||
|
use_amp=true
|
||||||
|
|
||||||
|
test-act-ete-eval-amp:
|
||||||
|
python lerobot/scripts/eval.py \
|
||||||
|
-p tests/outputs/act/checkpoints/000002 \
|
||||||
|
eval.n_episodes=1 \
|
||||||
|
eval.batch_size=1 \
|
||||||
|
env.episode_length=8 \
|
||||||
|
device=cpu \
|
||||||
|
use_amp=true
|
||||||
|
|
||||||
test-diffusion-ete-train:
|
test-diffusion-ete-train:
|
||||||
python lerobot/scripts/train.py \
|
python lerobot/scripts/train.py \
|
||||||
policy=diffusion \
|
policy=diffusion \
|
||||||
|
policy.down_dims=\[64,128,256\] \
|
||||||
|
policy.diffusion_step_embed_dim=32 \
|
||||||
|
policy.num_inference_steps=10 \
|
||||||
env=pusht \
|
env=pusht \
|
||||||
wandb.enable=False \
|
wandb.enable=False \
|
||||||
training.offline_steps=2 \
|
training.offline_steps=2 \
|
||||||
|
|
@ -74,6 +109,7 @@ test-diffusion-ete-eval:
|
||||||
env.episode_length=8 \
|
env.episode_length=8 \
|
||||||
device=cpu \
|
device=cpu \
|
||||||
|
|
||||||
|
# TODO(alexander-soare): Restore online_steps to 2 when it is reinstated.
|
||||||
test-tdmpc-ete-train:
|
test-tdmpc-ete-train:
|
||||||
python lerobot/scripts/train.py \
|
python lerobot/scripts/train.py \
|
||||||
policy=tdmpc \
|
policy=tdmpc \
|
||||||
|
|
@ -82,7 +118,7 @@ test-tdmpc-ete-train:
|
||||||
dataset_repo_id=lerobot/xarm_lift_medium \
|
dataset_repo_id=lerobot/xarm_lift_medium \
|
||||||
wandb.enable=False \
|
wandb.enable=False \
|
||||||
training.offline_steps=2 \
|
training.offline_steps=2 \
|
||||||
training.online_steps=2 \
|
training.online_steps=0 \
|
||||||
eval.n_episodes=1 \
|
eval.n_episodes=1 \
|
||||||
eval.batch_size=1 \
|
eval.batch_size=1 \
|
||||||
env.episode_length=2 \
|
env.episode_length=2 \
|
||||||
|
|
@ -100,7 +136,6 @@ test-tdmpc-ete-eval:
|
||||||
env.episode_length=8 \
|
env.episode_length=8 \
|
||||||
device=cpu \
|
device=cpu \
|
||||||
|
|
||||||
|
|
||||||
test-default-ete-eval:
|
test-default-ete-eval:
|
||||||
python lerobot/scripts/eval.py \
|
python lerobot/scripts/eval.py \
|
||||||
--config lerobot/configs/default.yaml \
|
--config lerobot/configs/default.yaml \
|
||||||
|
|
@ -108,3 +143,21 @@ test-default-ete-eval:
|
||||||
eval.batch_size=1 \
|
eval.batch_size=1 \
|
||||||
env.episode_length=8 \
|
env.episode_length=8 \
|
||||||
device=cpu \
|
device=cpu \
|
||||||
|
|
||||||
|
|
||||||
|
test-act-pusht-tutorial:
|
||||||
|
cp examples/advanced/1_train_act_pusht/act_pusht.yaml lerobot/configs/policy/created_by_Makefile.yaml
|
||||||
|
python lerobot/scripts/train.py \
|
||||||
|
policy=created_by_Makefile.yaml \
|
||||||
|
env=pusht \
|
||||||
|
wandb.enable=False \
|
||||||
|
training.offline_steps=2 \
|
||||||
|
eval.n_episodes=1 \
|
||||||
|
eval.batch_size=1 \
|
||||||
|
env.episode_length=2 \
|
||||||
|
device=cpu \
|
||||||
|
training.save_model=true \
|
||||||
|
training.save_freq=2 \
|
||||||
|
training.batch_size=2 \
|
||||||
|
hydra.run.dir=tests/outputs/act_pusht/
|
||||||
|
rm lerobot/configs/policy/created_by_Makefile.yaml
|
||||||
|
|
|
||||||
26
README.md
26
README.md
|
|
@ -77,6 +77,10 @@ Install 🤗 LeRobot:
|
||||||
pip install .
|
pip install .
|
||||||
```
|
```
|
||||||
|
|
||||||
|
> **NOTE:** Depending on your platform, If you encounter any build errors during this step
|
||||||
|
you may need to install `cmake` and `build-essential` for building some of our dependencies.
|
||||||
|
On linux: `sudo apt-get install cmake build-essential`
|
||||||
|
|
||||||
For simulations, 🤗 LeRobot comes with gymnasium environments that can be installed as extras:
|
For simulations, 🤗 LeRobot comes with gymnasium environments that can be installed as extras:
|
||||||
- [aloha](https://github.com/huggingface/gym-aloha)
|
- [aloha](https://github.com/huggingface/gym-aloha)
|
||||||
- [xarm](https://github.com/huggingface/gym-xarm)
|
- [xarm](https://github.com/huggingface/gym-xarm)
|
||||||
|
|
@ -99,6 +103,7 @@ wandb login
|
||||||
```
|
```
|
||||||
.
|
.
|
||||||
├── examples # contains demonstration examples, start here to learn about LeRobot
|
├── examples # contains demonstration examples, start here to learn about LeRobot
|
||||||
|
| └── advanced # contains even more examples for those who have mastered the basics
|
||||||
├── lerobot
|
├── lerobot
|
||||||
| ├── configs # contains hydra yaml files with all options that you can override in the command line
|
| ├── configs # contains hydra yaml files with all options that you can override in the command line
|
||||||
| | ├── default.yaml # selected by default, it loads pusht environment and diffusion policy
|
| | ├── default.yaml # selected by default, it loads pusht environment and diffusion policy
|
||||||
|
|
@ -158,9 +163,10 @@ See `python lerobot/scripts/eval.py --help` for more instructions.
|
||||||
|
|
||||||
### Train your own policy
|
### Train your own policy
|
||||||
|
|
||||||
Check out [example 3](./examples/3_train_policy.py) that illustrates how to start training a model.
|
Check out [example 3](./examples/3_train_policy.py) that illustrates how to train a model using our core library in python, and [example 4](./examples/4_train_policy_with_script.md) that shows how to use our training script from command line.
|
||||||
|
|
||||||
In general, you can use our training script to easily train any policy. Here is an example of training the ACT policy on trajectories collected by humans on the Aloha simulation environment for the insertion task:
|
In general, you can use our training script to easily train any policy. Here is an example of training the ACT policy on trajectories collected by humans on the Aloha simulation environment for the insertion task:
|
||||||
|
|
||||||
```bash
|
```bash
|
||||||
python lerobot/scripts/train.py \
|
python lerobot/scripts/train.py \
|
||||||
policy=act \
|
policy=act \
|
||||||
|
|
@ -184,7 +190,19 @@ A link to the wandb logs for the run will also show up in yellow in your termina
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. After training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `python lerobot/scripts/eval.py --help` for more instructions.
|
Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `python lerobot/scripts/eval.py --help` for more instructions.
|
||||||
|
|
||||||
|
#### Reproduce state-of-the-art (SOTA)
|
||||||
|
|
||||||
|
We have organized our configuration files (found under [`lerobot/configs`](./lerobot/configs)) such that they reproduce SOTA results from a given model variant in their respective original works. Simply running:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
python lerobot/scripts/train.py policy=diffusion env=pusht
|
||||||
|
```
|
||||||
|
|
||||||
|
reproduces SOTA results for Diffusion Policy on the PushT task.
|
||||||
|
|
||||||
|
Pretrained policies, along with reproduction details, can be found under the "Models" section of https://huggingface.co/lerobot.
|
||||||
|
|
||||||
## Contribute
|
## Contribute
|
||||||
|
|
||||||
|
|
@ -197,11 +215,11 @@ To add a dataset to the hub, you need to login using a write-access token, which
|
||||||
huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
|
huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
|
||||||
```
|
```
|
||||||
|
|
||||||
Then move your dataset folder in `data` directory (e.g. `data/aloha_ping_pong`), and push your dataset to the hub with:
|
Then move your dataset folder in `data` directory (e.g. `data/aloha_static_pingpong_test`), and push your dataset to the hub with:
|
||||||
```bash
|
```bash
|
||||||
python lerobot/scripts/push_dataset_to_hub.py \
|
python lerobot/scripts/push_dataset_to_hub.py \
|
||||||
--data-dir data \
|
--data-dir data \
|
||||||
--dataset-id aloha_ping_ping \
|
--dataset-id aloha_static_pingpong_test \
|
||||||
--raw-format aloha_hdf5 \
|
--raw-format aloha_hdf5 \
|
||||||
--community-id lerobot
|
--community-id lerobot
|
||||||
```
|
```
|
||||||
|
|
|
||||||
|
|
@ -0,0 +1,40 @@
|
||||||
|
FROM nvidia/cuda:12.4.1-base-ubuntu22.04
|
||||||
|
|
||||||
|
# Configure image
|
||||||
|
ARG PYTHON_VERSION=3.10
|
||||||
|
ARG DEBIAN_FRONTEND=noninteractive
|
||||||
|
|
||||||
|
# Install apt dependencies
|
||||||
|
RUN apt-get update && apt-get install -y --no-install-recommends \
|
||||||
|
build-essential cmake \
|
||||||
|
git git-lfs openssh-client \
|
||||||
|
nano vim less util-linux \
|
||||||
|
htop atop nvtop \
|
||||||
|
sed gawk grep curl wget \
|
||||||
|
tcpdump sysstat screen tmux \
|
||||||
|
libglib2.0-0 libgl1-mesa-glx libegl1-mesa ffmpeg \
|
||||||
|
python${PYTHON_VERSION} python${PYTHON_VERSION}-venv \
|
||||||
|
&& apt-get clean && rm -rf /var/lib/apt/lists/*
|
||||||
|
|
||||||
|
# Install gh cli tool
|
||||||
|
RUN (type -p wget >/dev/null || (apt update && apt-get install wget -y)) \
|
||||||
|
&& mkdir -p -m 755 /etc/apt/keyrings \
|
||||||
|
&& wget -qO- https://cli.github.com/packages/githubcli-archive-keyring.gpg | tee /etc/apt/keyrings/githubcli-archive-keyring.gpg > /dev/null \
|
||||||
|
&& chmod go+r /etc/apt/keyrings/githubcli-archive-keyring.gpg \
|
||||||
|
&& echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/githubcli-archive-keyring.gpg] https://cli.github.com/packages stable main" | tee /etc/apt/sources.list.d/github-cli.list > /dev/null \
|
||||||
|
&& apt update \
|
||||||
|
&& apt install gh -y \
|
||||||
|
&& apt clean && rm -rf /var/lib/apt/lists/*
|
||||||
|
|
||||||
|
# Setup `python`
|
||||||
|
RUN ln -s /usr/bin/python3 /usr/bin/python
|
||||||
|
|
||||||
|
# Install poetry
|
||||||
|
RUN curl -sSL https://install.python-poetry.org | python -
|
||||||
|
ENV PATH="/root/.local/bin:$PATH"
|
||||||
|
RUN echo 'if [ "$HOME" != "/root" ]; then ln -sf /root/.local/bin/poetry $HOME/.local/bin/poetry; fi' >> /root/.bashrc
|
||||||
|
RUN poetry config virtualenvs.create false
|
||||||
|
RUN poetry config virtualenvs.in-project true
|
||||||
|
|
||||||
|
# Set EGL as the rendering backend for MuJoCo
|
||||||
|
ENV MUJOCO_GL="egl"
|
||||||
|
|
@ -4,18 +4,15 @@ FROM nvidia/cuda:12.4.1-base-ubuntu22.04
|
||||||
ARG PYTHON_VERSION=3.10
|
ARG PYTHON_VERSION=3.10
|
||||||
ARG DEBIAN_FRONTEND=noninteractive
|
ARG DEBIAN_FRONTEND=noninteractive
|
||||||
|
|
||||||
|
|
||||||
# Install apt dependencies
|
# Install apt dependencies
|
||||||
RUN apt-get update && apt-get install -y --no-install-recommends \
|
RUN apt-get update && apt-get install -y --no-install-recommends \
|
||||||
build-essential cmake \
|
build-essential cmake \
|
||||||
git git-lfs openssh-client \
|
|
||||||
nano vim \
|
|
||||||
htop atop nvtop \
|
|
||||||
sed gawk grep curl wget \
|
|
||||||
tcpdump sysstat screen \
|
|
||||||
libglib2.0-0 libgl1-mesa-glx libegl1-mesa \
|
libglib2.0-0 libgl1-mesa-glx libegl1-mesa \
|
||||||
python${PYTHON_VERSION} python${PYTHON_VERSION}-venv \
|
python${PYTHON_VERSION} python${PYTHON_VERSION}-venv \
|
||||||
&& apt-get clean && rm -rf /var/lib/apt/lists/*
|
&& apt-get clean && rm -rf /var/lib/apt/lists/*
|
||||||
|
|
||||||
|
|
||||||
# Create virtual environment
|
# Create virtual environment
|
||||||
RUN ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python
|
RUN ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python
|
||||||
RUN python -m venv /opt/venv
|
RUN python -m venv /opt/venv
|
||||||
|
|
@ -23,8 +20,7 @@ ENV PATH="/opt/venv/bin:$PATH"
|
||||||
RUN echo "source /opt/venv/bin/activate" >> /root/.bashrc
|
RUN echo "source /opt/venv/bin/activate" >> /root/.bashrc
|
||||||
|
|
||||||
# Install LeRobot
|
# Install LeRobot
|
||||||
RUN git lfs install
|
COPY . /lerobot
|
||||||
RUN git clone https://github.com/huggingface/lerobot.git
|
|
||||||
WORKDIR /lerobot
|
WORKDIR /lerobot
|
||||||
RUN pip install --upgrade --no-cache-dir pip
|
RUN pip install --upgrade --no-cache-dir pip
|
||||||
RUN pip install --no-cache-dir ".[test, aloha, xarm, pusht]"
|
RUN pip install --no-cache-dir ".[test, aloha, xarm, pusht]"
|
||||||
|
|
|
||||||
|
|
@ -0,0 +1,183 @@
|
||||||
|
This tutorial will explain the training script, how to use it, and particularly the use of Hydra to configure everything needed for the training run.
|
||||||
|
|
||||||
|
## The training script
|
||||||
|
|
||||||
|
LeRobot offers a training script at [`lerobot/scripts/train.py`](../../lerobot/scripts/train.py). At a high level it does the following:
|
||||||
|
|
||||||
|
- Loads a Hydra configuration file for the following steps (more on Hydra in a moment).
|
||||||
|
- Makes a simulation environment.
|
||||||
|
- Makes a dataset corresponding to that simulation environment.
|
||||||
|
- Makes a policy.
|
||||||
|
- Runs a standard training loop with forward pass, backward pass, optimization step, and occasional logging, evaluation (of the policy on the environment), and checkpointing.
|
||||||
|
|
||||||
|
## Basics of how we use Hydra
|
||||||
|
|
||||||
|
Explaining the ins and outs of [Hydra](https://hydra.cc/docs/intro/) is beyond the scope of this document, but here we'll share the main points you need to know.
|
||||||
|
|
||||||
|
First, `lerobot/configs` has a directory structure like this:
|
||||||
|
|
||||||
|
```
|
||||||
|
.
|
||||||
|
├── default.yaml
|
||||||
|
├── env
|
||||||
|
│ ├── aloha.yaml
|
||||||
|
│ ├── pusht.yaml
|
||||||
|
│ └── xarm.yaml
|
||||||
|
└── policy
|
||||||
|
├── act.yaml
|
||||||
|
├── diffusion.yaml
|
||||||
|
└── tdmpc.yaml
|
||||||
|
```
|
||||||
|
|
||||||
|
**_For brevity, in the rest of this document we'll drop the leading `lerobot/configs` path. So `default.yaml` really refers to `lerobot/configs/default.yaml`._**
|
||||||
|
|
||||||
|
When you run the training script with
|
||||||
|
|
||||||
|
```python
|
||||||
|
python lerobot/scripts/train.py
|
||||||
|
```
|
||||||
|
|
||||||
|
Hydra is set up to read `default.yaml` (via the `@hydra.main` decorator). If you take a look at the `@hydra.main`'s arguments you will see `config_path="../configs", config_name="default"`. At the top of `default.yaml`, is a `defaults` section which looks likes this:
|
||||||
|
|
||||||
|
```yaml
|
||||||
|
defaults:
|
||||||
|
- _self_
|
||||||
|
- env: pusht
|
||||||
|
- policy: diffusion
|
||||||
|
```
|
||||||
|
|
||||||
|
This logic tells Hydra to incorporate configuration parameters from `env/pusht.yaml` and `policy/diffusion.yaml`. _Note: Be aware of the order as any configuration parameters with the same name will be overidden. Thus, `default.yaml` is overriden by `env/pusht.yaml` which is overidden by `policy/diffusion.yaml`_.
|
||||||
|
|
||||||
|
Then, `default.yaml` also contains common configuration parameters such as `device: cuda` or `use_amp: false` (for enabling fp16 training). Some other parameters are set to `???` which indicates that they are expected to be set in additional yaml files. For instance, `training.offline_steps: ???` in `default.yaml` is set to `200000` in `diffusion.yaml`.
|
||||||
|
|
||||||
|
Thanks to this `defaults` section in `default.yaml`, if you want to train Diffusion Policy with PushT, you really only need to run:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
python lerobot/scripts/train.py
|
||||||
|
```
|
||||||
|
|
||||||
|
However, you can be more explicit and launch the exact same Diffusion Policy training on PushT with:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
python lerobot/scripts/train.py policy=diffusion env=pusht
|
||||||
|
```
|
||||||
|
|
||||||
|
This way of overriding defaults via the CLI is especially useful when you want to change the policy and/or environment. For instance, you can train ACT on the default Aloha environment with:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
python lerobot/scripts/train.py policy=act env=aloha
|
||||||
|
```
|
||||||
|
|
||||||
|
There are two things to note here:
|
||||||
|
- Config overrides are passed as `param_name=param_value`.
|
||||||
|
- Here we have overridden the defaults section. `policy=act` tells Hydra to use `policy/act.yaml`, and `env=aloha` tells Hydra to use `env/pusht.yaml`.
|
||||||
|
|
||||||
|
_As an aside: we've set up all of our configurations so that they reproduce state-of-the-art results from papers in the literature._
|
||||||
|
|
||||||
|
## Overriding configuration parameters in the CLI
|
||||||
|
|
||||||
|
Now let's say that we want to train on a different task in the Aloha environment. If you look in `env/aloha.yaml` you will see something like:
|
||||||
|
|
||||||
|
```yaml
|
||||||
|
# lerobot/configs/env/aloha.yaml
|
||||||
|
env:
|
||||||
|
task: AlohaInsertion-v0
|
||||||
|
```
|
||||||
|
|
||||||
|
And if you look in `policy/act.yaml` you will see something like:
|
||||||
|
|
||||||
|
```yaml
|
||||||
|
# lerobot/configs/policy/act.yaml
|
||||||
|
dataset_repo_id: lerobot/aloha_sim_insertion_human
|
||||||
|
```
|
||||||
|
|
||||||
|
But our Aloha environment actually supports a cube transfer task as well. To train for this task, you could manually modify the two yaml configuration files respectively.
|
||||||
|
|
||||||
|
First, we'd need to switch to using the cube transfer task for the ALOHA environment.
|
||||||
|
|
||||||
|
```diff
|
||||||
|
# lerobot/configs/env/aloha.yaml
|
||||||
|
env:
|
||||||
|
- task: AlohaInsertion-v0
|
||||||
|
+ task: AlohaTransferCube-v0
|
||||||
|
```
|
||||||
|
|
||||||
|
Then, we'd also need to switch to using the cube transfer dataset.
|
||||||
|
|
||||||
|
```diff
|
||||||
|
# lerobot/configs/policy/act.yaml
|
||||||
|
-dataset_repo_id: lerobot/aloha_sim_insertion_human
|
||||||
|
+dataset_repo_id: lerobot/aloha_sim_transfer_cube_human
|
||||||
|
```
|
||||||
|
|
||||||
|
Then, you'd be able to run:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
python lerobot/scripts/train.py policy=act env=aloha
|
||||||
|
```
|
||||||
|
|
||||||
|
and you'd be training and evaluating on the cube transfer task.
|
||||||
|
|
||||||
|
An alternative approach to editing the yaml configuration files, would be to override the defaults via the command line:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
python lerobot/scripts/train.py \
|
||||||
|
policy=act \
|
||||||
|
dataset_repo_id=lerobot/aloha_sim_transfer_cube_human \
|
||||||
|
env=aloha \
|
||||||
|
env.task=AlohaTransferCube-v0
|
||||||
|
```
|
||||||
|
|
||||||
|
There's something new here. Notice the `.` delimiter used to traverse the configuration hierarchy. _But be aware that the `defaults` section is an exception. As you saw above, we didn't need to write `defaults.policy=act` in the CLI. `policy=act` was enough._
|
||||||
|
|
||||||
|
Putting all that knowledge together, here's the command that was used to train https://huggingface.co/lerobot/act_aloha_sim_transfer_cube_human.
|
||||||
|
|
||||||
|
```bash
|
||||||
|
python lerobot/scripts/train.py \
|
||||||
|
hydra.run.dir=outputs/train/act_aloha_sim_transfer_cube_human \
|
||||||
|
device=cuda
|
||||||
|
env=aloha \
|
||||||
|
env.task=AlohaTransferCube-v0 \
|
||||||
|
dataset_repo_id=lerobot/aloha_sim_transfer_cube_human \
|
||||||
|
policy=act \
|
||||||
|
training.eval_freq=10000 \
|
||||||
|
training.log_freq=250 \
|
||||||
|
training.offline_steps=100000 \
|
||||||
|
training.save_model=true \
|
||||||
|
training.save_freq=25000 \
|
||||||
|
eval.n_episodes=50 \
|
||||||
|
eval.batch_size=50 \
|
||||||
|
wandb.enable=false \
|
||||||
|
```
|
||||||
|
|
||||||
|
There's one new thing here: `hydra.run.dir=outputs/train/act_aloha_sim_transfer_cube_human`, which specifies where to save the training output.
|
||||||
|
|
||||||
|
## Using a configuration file not in `lerobot/configs`
|
||||||
|
|
||||||
|
Above we discusses the our training script is set up such that Hydra looks for `default.yaml` in `lerobot/configs`. But, if you have a configuration file elsewhere in your filesystem you may use:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
python lerobot/scripts/train.py --config-dir PARENT/PATH --config-name FILE_NAME_WITHOUT_EXTENSION
|
||||||
|
```
|
||||||
|
|
||||||
|
Note: here we use regular syntax for providing CLI arguments to a Python script, not Hydra's `param_name=param_value` syntax.
|
||||||
|
|
||||||
|
As a concrete example, this becomes particularly handy when you have a folder with training outputs, and would like to re-run the training. For example, say you previously ran the training script with one of the earlier commands and have `outputs/train/my_experiment/checkpoints/pretrained_model/config.yaml`. This `config.yaml` file will have the full set of configuration parameters within it. To run the training with the same configuration again, do:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
python lerobot/scripts/train.py --config-dir outputs/train/my_experiment/checkpoints/pretrained_model --config-name config
|
||||||
|
```
|
||||||
|
|
||||||
|
Note that you may still use the regular syntax for config parameter overrides (eg: by adding `training.offline_steps=200000`).
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
So far we've seen how to train Diffusion Policy for PushT and ACT for ALOHA. Now, what if we want to train ACT for PushT? Well, there are aspects of the ACT configuration that are specific to the ALOHA environments, and these happen to be incompatible with PushT. Therefore, trying to run the following will almost certainly raise an exception of sorts (eg: feature dimension mismatch):
|
||||||
|
|
||||||
|
```bash
|
||||||
|
python lerobot/scripts/train.py policy=act env=pusht dataset_repo_id=lerobot/pusht
|
||||||
|
```
|
||||||
|
|
||||||
|
Please, head on over to our [advanced tutorial on adapting policy configuration to various environments](./advanced/train_act_pusht/train_act_pusht.md) to learn more.
|
||||||
|
|
||||||
|
Or in the meantime, happy coding! 🤗
|
||||||
|
|
@ -0,0 +1,87 @@
|
||||||
|
# @package _global_
|
||||||
|
|
||||||
|
# Change the seed to match what PushT eval uses
|
||||||
|
# (to avoid evaluating on seeds used for generating the training data).
|
||||||
|
seed: 100000
|
||||||
|
# Change the dataset repository to the PushT one.
|
||||||
|
dataset_repo_id: lerobot/pusht
|
||||||
|
|
||||||
|
override_dataset_stats:
|
||||||
|
observation.image:
|
||||||
|
# stats from imagenet, since we use a pretrained vision model
|
||||||
|
mean: [[[0.485]], [[0.456]], [[0.406]]] # (c,1,1)
|
||||||
|
std: [[[0.229]], [[0.224]], [[0.225]]] # (c,1,1)
|
||||||
|
|
||||||
|
training:
|
||||||
|
offline_steps: 80000
|
||||||
|
online_steps: 0
|
||||||
|
eval_freq: 10000
|
||||||
|
save_freq: 100000
|
||||||
|
log_freq: 250
|
||||||
|
save_model: true
|
||||||
|
|
||||||
|
batch_size: 8
|
||||||
|
lr: 1e-5
|
||||||
|
lr_backbone: 1e-5
|
||||||
|
weight_decay: 1e-4
|
||||||
|
grad_clip_norm: 10
|
||||||
|
online_steps_between_rollouts: 1
|
||||||
|
|
||||||
|
delta_timestamps:
|
||||||
|
action: "[i / ${fps} for i in range(${policy.chunk_size})]"
|
||||||
|
|
||||||
|
eval:
|
||||||
|
n_episodes: 50
|
||||||
|
batch_size: 50
|
||||||
|
|
||||||
|
# See `configuration_act.py` for more details.
|
||||||
|
policy:
|
||||||
|
name: act
|
||||||
|
|
||||||
|
# Input / output structure.
|
||||||
|
n_obs_steps: 1
|
||||||
|
chunk_size: 100 # chunk_size
|
||||||
|
n_action_steps: 100
|
||||||
|
|
||||||
|
input_shapes:
|
||||||
|
observation.image: [3, 96, 96]
|
||||||
|
observation.state: ["${env.state_dim}"]
|
||||||
|
output_shapes:
|
||||||
|
action: ["${env.action_dim}"]
|
||||||
|
|
||||||
|
# Normalization / Unnormalization
|
||||||
|
input_normalization_modes:
|
||||||
|
observation.image: mean_std
|
||||||
|
# Use min_max normalization just because it's more standard.
|
||||||
|
observation.state: min_max
|
||||||
|
output_normalization_modes:
|
||||||
|
# Use min_max normalization just because it's more standard.
|
||||||
|
action: min_max
|
||||||
|
|
||||||
|
# Architecture.
|
||||||
|
# Vision backbone.
|
||||||
|
vision_backbone: resnet18
|
||||||
|
pretrained_backbone_weights: ResNet18_Weights.IMAGENET1K_V1
|
||||||
|
replace_final_stride_with_dilation: false
|
||||||
|
# Transformer layers.
|
||||||
|
pre_norm: false
|
||||||
|
dim_model: 512
|
||||||
|
n_heads: 8
|
||||||
|
dim_feedforward: 3200
|
||||||
|
feedforward_activation: relu
|
||||||
|
n_encoder_layers: 4
|
||||||
|
# Note: Although the original ACT implementation has 7 for `n_decoder_layers`, there is a bug in the code
|
||||||
|
# that means only the first layer is used. Here we match the original implementation by setting this to 1.
|
||||||
|
# See this issue https://github.com/tonyzhaozh/act/issues/25#issue-2258740521.
|
||||||
|
n_decoder_layers: 1
|
||||||
|
# VAE.
|
||||||
|
use_vae: true
|
||||||
|
latent_dim: 32
|
||||||
|
n_vae_encoder_layers: 4
|
||||||
|
|
||||||
|
# Inference.
|
||||||
|
temporal_ensemble_momentum: null
|
||||||
|
|
||||||
|
# Training and loss computation.
|
||||||
|
dropout: 0.1
|
||||||
|
kl_weight: 10.0
|
||||||
|
|
@ -0,0 +1,70 @@
|
||||||
|
In this tutorial we will learn how to adapt a policy configuration to be compatible with a new environment and dataset. As a concrete example, we will adapt the default configuration for ACT to be compatible with the PushT environment and dataset.
|
||||||
|
|
||||||
|
If you haven't already read our tutorial on the [training script and configuration tooling](../4_train_policy_with_script.md) please do so prior to tackling this tutorial.
|
||||||
|
|
||||||
|
Let's get started!
|
||||||
|
|
||||||
|
Suppose we want to train ACT for PushT. Well, there are aspects of the ACT configuration that are specific to the ALOHA environments, and these happen to be incompatible with PushT. Therefore, trying to run the following will almost certainly raise an exception of sorts (eg: feature dimension mismatch):
|
||||||
|
|
||||||
|
```bash
|
||||||
|
python lerobot/scripts/train.py policy=act env=pusht dataset_repo_id=lerobot/pusht
|
||||||
|
```
|
||||||
|
|
||||||
|
We need to adapt the parameters of the ACT policy configuration to the PushT environment. The most important ones are the image keys.
|
||||||
|
|
||||||
|
ALOHA's datasets and environments typically use a variable number of cameras. In `lerobot/configs/policy/act.yaml` you may notice two relevant sections. Here we show you the minimal diff needed to adjust to PushT:
|
||||||
|
|
||||||
|
```diff
|
||||||
|
override_dataset_stats:
|
||||||
|
- observation.images.top:
|
||||||
|
+ observation.image:
|
||||||
|
# stats from imagenet, since we use a pretrained vision model
|
||||||
|
mean: [[[0.485]], [[0.456]], [[0.406]]] # (c,1,1)
|
||||||
|
std: [[[0.229]], [[0.224]], [[0.225]]] # (c,1,1)
|
||||||
|
|
||||||
|
policy:
|
||||||
|
input_shapes:
|
||||||
|
- observation.images.top: [3, 480, 640]
|
||||||
|
+ observation.image: [3, 96, 96]
|
||||||
|
observation.state: ["${env.state_dim}"]
|
||||||
|
output_shapes:
|
||||||
|
action: ["${env.action_dim}"]
|
||||||
|
|
||||||
|
input_normalization_modes:
|
||||||
|
- observation.images.top: mean_std
|
||||||
|
+ observation.image: mean_std
|
||||||
|
observation.state: min_max
|
||||||
|
output_normalization_modes:
|
||||||
|
action: min_max
|
||||||
|
```
|
||||||
|
|
||||||
|
Here we've accounted for the following:
|
||||||
|
- PushT uses "observation.image" for its image key.
|
||||||
|
- PushT provides smaller images.
|
||||||
|
|
||||||
|
_Side note: technically we could override these via the CLI, but with many changes it gets a bit messy, and we also have a bit of a challenge in that we're using `.` in our observation keys which is treated by Hydra as a hierarchical separator_.
|
||||||
|
|
||||||
|
For your convenience, we provide [`act_pusht.yaml`](./act_pusht.yaml) in this directory. It contains the diff above, plus some other (optional) ones that are explained within. Please copy it into `lerobot/configs/policy` with:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
cp examples/advanced/1_train_act_pusht/act_pusht.yaml lerobot/configs/policy/act_pusht.yaml
|
||||||
|
```
|
||||||
|
|
||||||
|
(remember from a [previous tutorial](../4_train_policy_with_script.md) that Hydra will look in the `lerobot/configs` directory). Now try running the following.
|
||||||
|
|
||||||
|
<!-- Note to contributor: are you changing this command? Note that it's tested in `Makefile`, so change it there too! -->
|
||||||
|
```bash
|
||||||
|
python lerobot/scripts/train.py policy=act_pusht env=pusht
|
||||||
|
```
|
||||||
|
|
||||||
|
Notice that this is much the same as the command that failed at the start of the tutorial, only:
|
||||||
|
- Now we are using `policy=act_pusht` to point to our new configuration file.
|
||||||
|
- We can drop `dataset_repo_id=lerobot/pusht` as the change is incorporated in our new configuration file.
|
||||||
|
|
||||||
|
Hurrah! You're now training ACT for the PushT environment.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
The bottom line of this tutorial is that when training policies for different environments and datasets you will need to understand what parts of the policy configuration are specific to those and make changes accordingly.
|
||||||
|
|
||||||
|
Happy coding! 🤗
|
||||||
|
|
@ -0,0 +1,90 @@
|
||||||
|
"""This script demonstrates how to slice a dataset and calculate the loss on a subset of the data.
|
||||||
|
|
||||||
|
This technique can be useful for debugging and testing purposes, as well as identifying whether a policy
|
||||||
|
is learning effectively.
|
||||||
|
|
||||||
|
Furthermore, relying on validation loss to evaluate performance is generally not considered a good practice,
|
||||||
|
especially in the context of imitation learning. The most reliable approach is to evaluate the policy directly
|
||||||
|
on the target environment, whether that be in simulation or the real world.
|
||||||
|
"""
|
||||||
|
|
||||||
|
import math
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
import torch
|
||||||
|
from huggingface_hub import snapshot_download
|
||||||
|
|
||||||
|
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
|
||||||
|
from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
|
||||||
|
|
||||||
|
device = torch.device("cuda")
|
||||||
|
|
||||||
|
# Download the diffusion policy for pusht environment
|
||||||
|
pretrained_policy_path = Path(snapshot_download("lerobot/diffusion_pusht"))
|
||||||
|
# OR uncomment the following to evaluate a policy from the local outputs/train folder.
|
||||||
|
# pretrained_policy_path = Path("outputs/train/example_pusht_diffusion")
|
||||||
|
|
||||||
|
policy = DiffusionPolicy.from_pretrained(pretrained_policy_path)
|
||||||
|
policy.eval()
|
||||||
|
policy.to(device)
|
||||||
|
|
||||||
|
# Set up the dataset.
|
||||||
|
delta_timestamps = {
|
||||||
|
# Load the previous image and state at -0.1 seconds before current frame,
|
||||||
|
# then load current image and state corresponding to 0.0 second.
|
||||||
|
"observation.image": [-0.1, 0.0],
|
||||||
|
"observation.state": [-0.1, 0.0],
|
||||||
|
# Load the previous action (-0.1), the next action to be executed (0.0),
|
||||||
|
# and 14 future actions with a 0.1 seconds spacing. All these actions will be
|
||||||
|
# used to calculate the loss.
|
||||||
|
"action": [-0.1, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4],
|
||||||
|
}
|
||||||
|
|
||||||
|
# Load the last 10% of episodes of the dataset as a validation set.
|
||||||
|
# - Load full dataset
|
||||||
|
full_dataset = LeRobotDataset("lerobot/pusht", split="train")
|
||||||
|
# - Calculate train and val subsets
|
||||||
|
num_train_episodes = math.floor(full_dataset.num_episodes * 90 / 100)
|
||||||
|
num_val_episodes = full_dataset.num_episodes - num_train_episodes
|
||||||
|
print(f"Number of episodes in full dataset: {full_dataset.num_episodes}")
|
||||||
|
print(f"Number of episodes in training dataset (90% subset): {num_train_episodes}")
|
||||||
|
print(f"Number of episodes in validation dataset (10% subset): {num_val_episodes}")
|
||||||
|
# - Get first frame index of the validation set
|
||||||
|
first_val_frame_index = full_dataset.episode_data_index["from"][num_train_episodes].item()
|
||||||
|
# - Load frames subset belonging to validation set using the `split` argument.
|
||||||
|
# It utilizes the `datasets` library's syntax for slicing datasets.
|
||||||
|
# For more information on the Slice API, please see:
|
||||||
|
# https://huggingface.co/docs/datasets/v2.19.0/loading#slice-splits
|
||||||
|
train_dataset = LeRobotDataset(
|
||||||
|
"lerobot/pusht", split=f"train[:{first_val_frame_index}]", delta_timestamps=delta_timestamps
|
||||||
|
)
|
||||||
|
val_dataset = LeRobotDataset(
|
||||||
|
"lerobot/pusht", split=f"train[{first_val_frame_index}:]", delta_timestamps=delta_timestamps
|
||||||
|
)
|
||||||
|
print(f"Number of frames in training dataset (90% subset): {len(train_dataset)}")
|
||||||
|
print(f"Number of frames in validation dataset (10% subset): {len(val_dataset)}")
|
||||||
|
|
||||||
|
# Create dataloader for evaluation.
|
||||||
|
val_dataloader = torch.utils.data.DataLoader(
|
||||||
|
val_dataset,
|
||||||
|
num_workers=4,
|
||||||
|
batch_size=64,
|
||||||
|
shuffle=False,
|
||||||
|
pin_memory=device != torch.device("cpu"),
|
||||||
|
drop_last=False,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Run validation loop.
|
||||||
|
loss_cumsum = 0
|
||||||
|
n_examples_evaluated = 0
|
||||||
|
for batch in val_dataloader:
|
||||||
|
batch = {k: v.to(device, non_blocking=True) for k, v in batch.items()}
|
||||||
|
output_dict = policy.forward(batch)
|
||||||
|
|
||||||
|
loss_cumsum += output_dict["loss"].item()
|
||||||
|
n_examples_evaluated += batch["index"].shape[0]
|
||||||
|
|
||||||
|
# Calculate the average loss over the validation set.
|
||||||
|
average_loss = loss_cumsum / n_examples_evaluated
|
||||||
|
|
||||||
|
print(f"Average loss on validation set: {average_loss:.4f}")
|
||||||
|
|
@ -61,13 +61,21 @@ available_datasets_per_env = {
|
||||||
"lerobot/aloha_sim_insertion_scripted",
|
"lerobot/aloha_sim_insertion_scripted",
|
||||||
"lerobot/aloha_sim_transfer_cube_human",
|
"lerobot/aloha_sim_transfer_cube_human",
|
||||||
"lerobot/aloha_sim_transfer_cube_scripted",
|
"lerobot/aloha_sim_transfer_cube_scripted",
|
||||||
|
"lerobot/aloha_sim_insertion_human_image",
|
||||||
|
"lerobot/aloha_sim_insertion_scripted_image",
|
||||||
|
"lerobot/aloha_sim_transfer_cube_human_image",
|
||||||
|
"lerobot/aloha_sim_transfer_cube_scripted_image",
|
||||||
],
|
],
|
||||||
"pusht": ["lerobot/pusht"],
|
"pusht": ["lerobot/pusht", "lerobot/pusht_image"],
|
||||||
"xarm": [
|
"xarm": [
|
||||||
"lerobot/xarm_lift_medium",
|
"lerobot/xarm_lift_medium",
|
||||||
"lerobot/xarm_lift_medium_replay",
|
"lerobot/xarm_lift_medium_replay",
|
||||||
"lerobot/xarm_push_medium",
|
"lerobot/xarm_push_medium",
|
||||||
"lerobot/xarm_push_medium_replay",
|
"lerobot/xarm_push_medium_replay",
|
||||||
|
"lerobot/xarm_lift_medium_image",
|
||||||
|
"lerobot/xarm_lift_medium_replay_image",
|
||||||
|
"lerobot/xarm_push_medium_image",
|
||||||
|
"lerobot/xarm_push_medium_replay_image",
|
||||||
],
|
],
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -20,17 +20,19 @@ import datasets
|
||||||
import torch
|
import torch
|
||||||
|
|
||||||
from lerobot.common.datasets.utils import (
|
from lerobot.common.datasets.utils import (
|
||||||
|
calculate_episode_data_index,
|
||||||
load_episode_data_index,
|
load_episode_data_index,
|
||||||
load_hf_dataset,
|
load_hf_dataset,
|
||||||
load_info,
|
load_info,
|
||||||
load_previous_and_future_frames,
|
load_previous_and_future_frames,
|
||||||
load_stats,
|
load_stats,
|
||||||
load_videos,
|
load_videos,
|
||||||
|
reset_episode_index,
|
||||||
)
|
)
|
||||||
from lerobot.common.datasets.video_utils import VideoFrame, load_from_videos
|
from lerobot.common.datasets.video_utils import VideoFrame, load_from_videos
|
||||||
|
|
||||||
DATA_DIR = Path(os.environ["DATA_DIR"]) if "DATA_DIR" in os.environ else None
|
DATA_DIR = Path(os.environ["DATA_DIR"]) if "DATA_DIR" in os.environ else None
|
||||||
CODEBASE_VERSION = "v1.3"
|
CODEBASE_VERSION = "v1.4"
|
||||||
|
|
||||||
|
|
||||||
class LeRobotDataset(torch.utils.data.Dataset):
|
class LeRobotDataset(torch.utils.data.Dataset):
|
||||||
|
|
@ -73,7 +75,11 @@ class LeRobotDataset(torch.utils.data.Dataset):
|
||||||
# TODO(rcadene, aliberts): implement faster transfer
|
# TODO(rcadene, aliberts): implement faster transfer
|
||||||
# https://huggingface.co/docs/huggingface_hub/en/guides/download#faster-downloads
|
# https://huggingface.co/docs/huggingface_hub/en/guides/download#faster-downloads
|
||||||
self.hf_dataset = load_hf_dataset(repo_id, version, root, split)
|
self.hf_dataset = load_hf_dataset(repo_id, version, root, split)
|
||||||
|
if split == "train":
|
||||||
self.episode_data_index = load_episode_data_index(repo_id, version, root)
|
self.episode_data_index = load_episode_data_index(repo_id, version, root)
|
||||||
|
else:
|
||||||
|
self.episode_data_index = calculate_episode_data_index(self.hf_dataset)
|
||||||
|
self.hf_dataset = reset_episode_index(self.hf_dataset)
|
||||||
self.stats = load_stats(repo_id, version, root)
|
self.stats = load_stats(repo_id, version, root)
|
||||||
self.info = load_info(repo_id, version, root)
|
self.info = load_info(repo_id, version, root)
|
||||||
if self.video:
|
if self.video:
|
||||||
|
|
|
||||||
|
|
@ -24,17 +24,16 @@ import shutil
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
|
|
||||||
import tqdm
|
import tqdm
|
||||||
|
from huggingface_hub import snapshot_download
|
||||||
ALOHA_RAW_URLS_DIR = "lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls"
|
|
||||||
|
|
||||||
|
|
||||||
def download_raw(raw_dir, dataset_id):
|
def download_raw(raw_dir, dataset_id):
|
||||||
if "pusht" in dataset_id:
|
if "aloha" in dataset_id or "image" in dataset_id:
|
||||||
|
download_hub(raw_dir, dataset_id)
|
||||||
|
elif "pusht" in dataset_id:
|
||||||
download_pusht(raw_dir)
|
download_pusht(raw_dir)
|
||||||
elif "xarm" in dataset_id:
|
elif "xarm" in dataset_id:
|
||||||
download_xarm(raw_dir)
|
download_xarm(raw_dir)
|
||||||
elif "aloha" in dataset_id:
|
|
||||||
download_aloha(raw_dir, dataset_id)
|
|
||||||
elif "umi" in dataset_id:
|
elif "umi" in dataset_id:
|
||||||
download_umi(raw_dir)
|
download_umi(raw_dir)
|
||||||
else:
|
else:
|
||||||
|
|
@ -103,37 +102,13 @@ def download_xarm(raw_dir: Path):
|
||||||
zip_path.unlink()
|
zip_path.unlink()
|
||||||
|
|
||||||
|
|
||||||
def download_aloha(raw_dir: Path, dataset_id: str):
|
def download_hub(raw_dir: Path, dataset_id: str):
|
||||||
import gdown
|
|
||||||
|
|
||||||
subset_id = dataset_id.replace("aloha_", "")
|
|
||||||
urls_path = Path(ALOHA_RAW_URLS_DIR) / f"{subset_id}.txt"
|
|
||||||
assert urls_path.exists(), f"{subset_id}.txt not found in '{ALOHA_RAW_URLS_DIR}' directory."
|
|
||||||
|
|
||||||
with open(urls_path) as f:
|
|
||||||
# strip lines and ignore empty lines
|
|
||||||
urls = [url.strip() for url in f if url.strip()]
|
|
||||||
|
|
||||||
# sanity check
|
|
||||||
for url in urls:
|
|
||||||
assert (
|
|
||||||
"drive.google.com/drive/folders" in url or "drive.google.com/file" in url
|
|
||||||
), f"Wrong url provided '{url}' in file '{urls_path}'."
|
|
||||||
|
|
||||||
raw_dir = Path(raw_dir)
|
raw_dir = Path(raw_dir)
|
||||||
raw_dir.mkdir(parents=True, exist_ok=True)
|
raw_dir.mkdir(parents=True, exist_ok=True)
|
||||||
|
|
||||||
logging.info(f"Start downloading from google drive for {dataset_id}")
|
logging.info(f"Start downloading from huggingface.co/cadene for {dataset_id}")
|
||||||
for url in urls:
|
snapshot_download(f"cadene/{dataset_id}_raw", repo_type="dataset", local_dir=raw_dir)
|
||||||
if "drive.google.com/drive/folders" in url:
|
logging.info(f"Finish downloading from huggingface.co/cadene for {dataset_id}")
|
||||||
# when a folder url is given, download up to 50 files from the folder
|
|
||||||
gdown.download_folder(url, output=str(raw_dir), remaining_ok=True)
|
|
||||||
|
|
||||||
elif "drive.google.com/file" in url:
|
|
||||||
# because of the 50 files limit per folder, we download the remaining files (file by file)
|
|
||||||
gdown.download(url, output=str(raw_dir), fuzzy=True)
|
|
||||||
|
|
||||||
logging.info(f"End downloading from google drive for {dataset_id}")
|
|
||||||
|
|
||||||
|
|
||||||
def download_umi(raw_dir: Path):
|
def download_umi(raw_dir: Path):
|
||||||
|
|
@ -148,21 +123,30 @@ def download_umi(raw_dir: Path):
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
data_dir = Path("data")
|
data_dir = Path("data")
|
||||||
dataset_ids = [
|
dataset_ids = [
|
||||||
|
"pusht_image",
|
||||||
|
"xarm_lift_medium_image",
|
||||||
|
"xarm_lift_medium_replay_image",
|
||||||
|
"xarm_push_medium_image",
|
||||||
|
"xarm_push_medium_replay_image",
|
||||||
|
"aloha_sim_insertion_human_image",
|
||||||
|
"aloha_sim_insertion_scripted_image",
|
||||||
|
"aloha_sim_transfer_cube_human_image",
|
||||||
|
"aloha_sim_transfer_cube_scripted_image",
|
||||||
"pusht",
|
"pusht",
|
||||||
"xarm_lift_medium",
|
"xarm_lift_medium",
|
||||||
"xarm_lift_medium_replay",
|
"xarm_lift_medium_replay",
|
||||||
"xarm_push_medium",
|
"xarm_push_medium",
|
||||||
"xarm_push_medium_replay",
|
"xarm_push_medium_replay",
|
||||||
|
"aloha_sim_insertion_human",
|
||||||
|
"aloha_sim_insertion_scripted",
|
||||||
|
"aloha_sim_transfer_cube_human",
|
||||||
|
"aloha_sim_transfer_cube_scripted",
|
||||||
"aloha_mobile_cabinet",
|
"aloha_mobile_cabinet",
|
||||||
"aloha_mobile_chair",
|
"aloha_mobile_chair",
|
||||||
"aloha_mobile_elevator",
|
"aloha_mobile_elevator",
|
||||||
"aloha_mobile_shrimp",
|
"aloha_mobile_shrimp",
|
||||||
"aloha_mobile_wash_pan",
|
"aloha_mobile_wash_pan",
|
||||||
"aloha_mobile_wipe_wine",
|
"aloha_mobile_wipe_wine",
|
||||||
"aloha_sim_insertion_human",
|
|
||||||
"aloha_sim_insertion_scripted",
|
|
||||||
"aloha_sim_transfer_cube_human",
|
|
||||||
"aloha_sim_transfer_cube_scripted",
|
|
||||||
"aloha_static_battery",
|
"aloha_static_battery",
|
||||||
"aloha_static_candy",
|
"aloha_static_candy",
|
||||||
"aloha_static_coffee",
|
"aloha_static_coffee",
|
||||||
|
|
|
||||||
|
|
@ -17,7 +17,7 @@
|
||||||
Contains utilities to process raw data format of HDF5 files like in: https://github.com/tonyzhaozh/act
|
Contains utilities to process raw data format of HDF5 files like in: https://github.com/tonyzhaozh/act
|
||||||
"""
|
"""
|
||||||
|
|
||||||
import re
|
import gc
|
||||||
import shutil
|
import shutil
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
|
|
||||||
|
|
@ -79,10 +79,8 @@ def load_from_raw(raw_dir, out_dir, fps, video, debug):
|
||||||
episode_data_index = {"from": [], "to": []}
|
episode_data_index = {"from": [], "to": []}
|
||||||
|
|
||||||
id_from = 0
|
id_from = 0
|
||||||
|
for ep_idx, ep_path in tqdm.tqdm(enumerate(hdf5_files), total=len(hdf5_files)):
|
||||||
for ep_path in tqdm.tqdm(hdf5_files, total=len(hdf5_files)):
|
|
||||||
with h5py.File(ep_path, "r") as ep:
|
with h5py.File(ep_path, "r") as ep:
|
||||||
ep_idx = int(re.search(r"episode_(\d+)", ep_path.name).group(1))
|
|
||||||
num_frames = ep["/action"].shape[0]
|
num_frames = ep["/action"].shape[0]
|
||||||
|
|
||||||
# last step of demonstration is considered done
|
# last step of demonstration is considered done
|
||||||
|
|
@ -91,6 +89,10 @@ def load_from_raw(raw_dir, out_dir, fps, video, debug):
|
||||||
|
|
||||||
state = torch.from_numpy(ep["/observations/qpos"][:])
|
state = torch.from_numpy(ep["/observations/qpos"][:])
|
||||||
action = torch.from_numpy(ep["/action"][:])
|
action = torch.from_numpy(ep["/action"][:])
|
||||||
|
if "/observations/qvel" in ep:
|
||||||
|
velocity = torch.from_numpy(ep["/observations/qvel"][:])
|
||||||
|
if "/observations/effort" in ep:
|
||||||
|
effort = torch.from_numpy(ep["/observations/effort"][:])
|
||||||
|
|
||||||
ep_dict = {}
|
ep_dict = {}
|
||||||
|
|
||||||
|
|
@ -131,6 +133,10 @@ def load_from_raw(raw_dir, out_dir, fps, video, debug):
|
||||||
ep_dict[img_key] = [PILImage.fromarray(x) for x in imgs_array]
|
ep_dict[img_key] = [PILImage.fromarray(x) for x in imgs_array]
|
||||||
|
|
||||||
ep_dict["observation.state"] = state
|
ep_dict["observation.state"] = state
|
||||||
|
if "/observations/velocity" in ep:
|
||||||
|
ep_dict["observation.velocity"] = velocity
|
||||||
|
if "/observations/effort" in ep:
|
||||||
|
ep_dict["observation.effort"] = effort
|
||||||
ep_dict["action"] = action
|
ep_dict["action"] = action
|
||||||
ep_dict["episode_index"] = torch.tensor([ep_idx] * num_frames)
|
ep_dict["episode_index"] = torch.tensor([ep_idx] * num_frames)
|
||||||
ep_dict["frame_index"] = torch.arange(0, num_frames, 1)
|
ep_dict["frame_index"] = torch.arange(0, num_frames, 1)
|
||||||
|
|
@ -146,6 +152,8 @@ def load_from_raw(raw_dir, out_dir, fps, video, debug):
|
||||||
|
|
||||||
id_from += num_frames
|
id_from += num_frames
|
||||||
|
|
||||||
|
gc.collect()
|
||||||
|
|
||||||
# process first episode only
|
# process first episode only
|
||||||
if debug:
|
if debug:
|
||||||
break
|
break
|
||||||
|
|
@ -167,6 +175,14 @@ def to_hf_dataset(data_dict, video) -> Dataset:
|
||||||
features["observation.state"] = Sequence(
|
features["observation.state"] = Sequence(
|
||||||
length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
|
length=data_dict["observation.state"].shape[1], feature=Value(dtype="float32", id=None)
|
||||||
)
|
)
|
||||||
|
if "observation.velocity" in data_dict:
|
||||||
|
features["observation.velocity"] = Sequence(
|
||||||
|
length=data_dict["observation.velocity"].shape[1], feature=Value(dtype="float32", id=None)
|
||||||
|
)
|
||||||
|
if "observation.effort" in data_dict:
|
||||||
|
features["observation.effort"] = Sequence(
|
||||||
|
length=data_dict["observation.effort"].shape[1], feature=Value(dtype="float32", id=None)
|
||||||
|
)
|
||||||
features["action"] = Sequence(
|
features["action"] = Sequence(
|
||||||
length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)
|
length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)
|
||||||
)
|
)
|
||||||
|
|
|
||||||
|
|
@ -14,7 +14,9 @@
|
||||||
# See the License for the specific language governing permissions and
|
# See the License for the specific language governing permissions and
|
||||||
# limitations under the License.
|
# limitations under the License.
|
||||||
import json
|
import json
|
||||||
|
import re
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
|
from typing import Dict
|
||||||
|
|
||||||
import datasets
|
import datasets
|
||||||
import torch
|
import torch
|
||||||
|
|
@ -79,7 +81,23 @@ def hf_transform_to_torch(items_dict):
|
||||||
def load_hf_dataset(repo_id, version, root, split) -> datasets.Dataset:
|
def load_hf_dataset(repo_id, version, root, split) -> datasets.Dataset:
|
||||||
"""hf_dataset contains all the observations, states, actions, rewards, etc."""
|
"""hf_dataset contains all the observations, states, actions, rewards, etc."""
|
||||||
if root is not None:
|
if root is not None:
|
||||||
hf_dataset = load_from_disk(str(Path(root) / repo_id / split))
|
hf_dataset = load_from_disk(str(Path(root) / repo_id / "train"))
|
||||||
|
# TODO(rcadene): clean this which enables getting a subset of dataset
|
||||||
|
if split != "train":
|
||||||
|
if "%" in split:
|
||||||
|
raise NotImplementedError(f"We dont support splitting based on percentage for now ({split}).")
|
||||||
|
match_from = re.search(r"train\[(\d+):\]", split)
|
||||||
|
match_to = re.search(r"train\[:(\d+)\]", split)
|
||||||
|
if match_from:
|
||||||
|
from_frame_index = int(match_from.group(1))
|
||||||
|
hf_dataset = hf_dataset.select(range(from_frame_index, len(hf_dataset)))
|
||||||
|
elif match_to:
|
||||||
|
to_frame_index = int(match_to.group(1))
|
||||||
|
hf_dataset = hf_dataset.select(range(to_frame_index))
|
||||||
|
else:
|
||||||
|
raise ValueError(
|
||||||
|
f'`split` ({split}) should either be "train", "train[INT:]", or "train[:INT]"'
|
||||||
|
)
|
||||||
else:
|
else:
|
||||||
hf_dataset = load_dataset(repo_id, revision=version, split=split)
|
hf_dataset = load_dataset(repo_id, revision=version, split=split)
|
||||||
hf_dataset.set_transform(hf_transform_to_torch)
|
hf_dataset.set_transform(hf_transform_to_torch)
|
||||||
|
|
@ -245,6 +263,84 @@ def load_previous_and_future_frames(
|
||||||
return item
|
return item
|
||||||
|
|
||||||
|
|
||||||
|
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
|
||||||
|
|
||||||
|
|
||||||
|
def reset_episode_index(hf_dataset: datasets.Dataset) -> datasets.Dataset:
|
||||||
|
"""
|
||||||
|
Reset the `episode_index` of the provided HuggingFace Dataset.
|
||||||
|
|
||||||
|
`episode_data_index` (and related functionality such as `load_previous_and_future_frames`) requires the
|
||||||
|
`episode_index` to be sorted, continuous (1,1,1 and not 1,2,1) and start at 0.
|
||||||
|
|
||||||
|
This brings the `episode_index` to the required format.
|
||||||
|
"""
|
||||||
|
if len(hf_dataset) == 0:
|
||||||
|
return hf_dataset
|
||||||
|
unique_episode_idxs = torch.stack(hf_dataset["episode_index"]).unique().tolist()
|
||||||
|
episode_idx_to_reset_idx_mapping = {
|
||||||
|
ep_id: reset_ep_id for reset_ep_id, ep_id in enumerate(unique_episode_idxs)
|
||||||
|
}
|
||||||
|
|
||||||
|
def modify_ep_idx_func(example):
|
||||||
|
example["episode_index"] = episode_idx_to_reset_idx_mapping[example["episode_index"].item()]
|
||||||
|
return example
|
||||||
|
|
||||||
|
hf_dataset = hf_dataset.map(modify_ep_idx_func)
|
||||||
|
return hf_dataset
|
||||||
|
|
||||||
|
|
||||||
def cycle(iterable):
|
def cycle(iterable):
|
||||||
"""The equivalent of itertools.cycle, but safe for Pytorch dataloaders.
|
"""The equivalent of itertools.cycle, but safe for Pytorch dataloaders.
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -304,7 +304,11 @@ class DiffusionModel(nn.Module):
|
||||||
loss = F.mse_loss(pred, target, reduction="none")
|
loss = F.mse_loss(pred, target, reduction="none")
|
||||||
|
|
||||||
# Mask loss wherever the action is padded with copies (edges of the dataset trajectory).
|
# Mask loss wherever the action is padded with copies (edges of the dataset trajectory).
|
||||||
if self.config.do_mask_loss_for_padding and "action_is_pad" in batch:
|
if self.config.do_mask_loss_for_padding:
|
||||||
|
if "action_is_pad" not in batch:
|
||||||
|
raise ValueError(
|
||||||
|
f"You need to provide 'action_is_pad' in the batch when {self.config.do_mask_loss_for_padding=}."
|
||||||
|
)
|
||||||
in_episode_bound = ~batch["action_is_pad"]
|
in_episode_bound = ~batch["action_is_pad"]
|
||||||
loss = loss * in_episode_bound.unsqueeze(-1)
|
loss = loss * in_episode_bound.unsqueeze(-1)
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -10,6 +10,9 @@ hydra:
|
||||||
name: default
|
name: default
|
||||||
|
|
||||||
device: cuda # cpu
|
device: cuda # cpu
|
||||||
|
# `use_amp` determines whether to use Automatic Mixed Precision (AMP) for training and evaluation. With AMP,
|
||||||
|
# automatic gradient scaling is used.
|
||||||
|
use_amp: false
|
||||||
# `seed` is used for training (eg: model initialization, dataset shuffling)
|
# `seed` is used for training (eg: model initialization, dataset shuffling)
|
||||||
# AND for the evaluation environments.
|
# AND for the evaluation environments.
|
||||||
seed: ???
|
seed: ???
|
||||||
|
|
@ -17,6 +20,7 @@ dataset_repo_id: lerobot/pusht
|
||||||
|
|
||||||
training:
|
training:
|
||||||
offline_steps: ???
|
offline_steps: ???
|
||||||
|
# NOTE: `online_steps` is not implemented yet. It's here as a placeholder.
|
||||||
online_steps: ???
|
online_steps: ???
|
||||||
online_steps_between_rollouts: ???
|
online_steps_between_rollouts: ???
|
||||||
online_sampling_ratio: 0.5
|
online_sampling_ratio: 0.5
|
||||||
|
|
|
||||||
|
|
@ -5,7 +5,8 @@ dataset_repo_id: lerobot/xarm_lift_medium
|
||||||
|
|
||||||
training:
|
training:
|
||||||
offline_steps: 25000
|
offline_steps: 25000
|
||||||
online_steps: 25000
|
# TODO(alexander-soare): uncomment when online training gets reinstated
|
||||||
|
online_steps: 0 # 25000 not implemented yet
|
||||||
eval_freq: 5000
|
eval_freq: 5000
|
||||||
online_steps_between_rollouts: 1
|
online_steps_between_rollouts: 1
|
||||||
online_sampling_ratio: 0.5
|
online_sampling_ratio: 0.5
|
||||||
|
|
|
||||||
|
|
@ -46,6 +46,7 @@ import json
|
||||||
import logging
|
import logging
|
||||||
import threading
|
import threading
|
||||||
import time
|
import time
|
||||||
|
from contextlib import nullcontext
|
||||||
from copy import deepcopy
|
from copy import deepcopy
|
||||||
from datetime import datetime as dt
|
from datetime import datetime as dt
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
|
|
@ -520,7 +521,7 @@ def eval(
|
||||||
raise NotImplementedError()
|
raise NotImplementedError()
|
||||||
|
|
||||||
# Check device is available
|
# Check device is available
|
||||||
get_safe_torch_device(hydra_cfg.device, log=True)
|
device = get_safe_torch_device(hydra_cfg.device, log=True)
|
||||||
|
|
||||||
torch.backends.cudnn.benchmark = True
|
torch.backends.cudnn.benchmark = True
|
||||||
torch.backends.cuda.matmul.allow_tf32 = True
|
torch.backends.cuda.matmul.allow_tf32 = True
|
||||||
|
|
@ -539,6 +540,7 @@ def eval(
|
||||||
policy = make_policy(hydra_cfg=hydra_cfg, dataset_stats=make_dataset(hydra_cfg).stats)
|
policy = make_policy(hydra_cfg=hydra_cfg, dataset_stats=make_dataset(hydra_cfg).stats)
|
||||||
policy.eval()
|
policy.eval()
|
||||||
|
|
||||||
|
with torch.no_grad(), torch.autocast(device_type=device.type) if hydra_cfg.use_amp else nullcontext():
|
||||||
info = eval_policy(
|
info = eval_policy(
|
||||||
env,
|
env,
|
||||||
policy,
|
policy,
|
||||||
|
|
|
||||||
|
|
@ -25,7 +25,6 @@ python lerobot/scripts/push_dataset_to_hub.py \
|
||||||
--dataset-id pusht \
|
--dataset-id pusht \
|
||||||
--raw-format pusht_zarr \
|
--raw-format pusht_zarr \
|
||||||
--community-id lerobot \
|
--community-id lerobot \
|
||||||
--revision v1.2 \
|
|
||||||
--dry-run 1 \
|
--dry-run 1 \
|
||||||
--save-to-disk 1 \
|
--save-to-disk 1 \
|
||||||
--save-tests-to-disk 0 \
|
--save-tests-to-disk 0 \
|
||||||
|
|
@ -36,7 +35,6 @@ python lerobot/scripts/push_dataset_to_hub.py \
|
||||||
--dataset-id xarm_lift_medium \
|
--dataset-id xarm_lift_medium \
|
||||||
--raw-format xarm_pkl \
|
--raw-format xarm_pkl \
|
||||||
--community-id lerobot \
|
--community-id lerobot \
|
||||||
--revision v1.2 \
|
|
||||||
--dry-run 1 \
|
--dry-run 1 \
|
||||||
--save-to-disk 1 \
|
--save-to-disk 1 \
|
||||||
--save-tests-to-disk 0 \
|
--save-tests-to-disk 0 \
|
||||||
|
|
@ -47,7 +45,6 @@ python lerobot/scripts/push_dataset_to_hub.py \
|
||||||
--dataset-id aloha_sim_insertion_scripted \
|
--dataset-id aloha_sim_insertion_scripted \
|
||||||
--raw-format aloha_hdf5 \
|
--raw-format aloha_hdf5 \
|
||||||
--community-id lerobot \
|
--community-id lerobot \
|
||||||
--revision v1.2 \
|
|
||||||
--dry-run 1 \
|
--dry-run 1 \
|
||||||
--save-to-disk 1 \
|
--save-to-disk 1 \
|
||||||
--save-tests-to-disk 0 \
|
--save-tests-to-disk 0 \
|
||||||
|
|
@ -58,7 +55,6 @@ python lerobot/scripts/push_dataset_to_hub.py \
|
||||||
--dataset-id umi_cup_in_the_wild \
|
--dataset-id umi_cup_in_the_wild \
|
||||||
--raw-format umi_zarr \
|
--raw-format umi_zarr \
|
||||||
--community-id lerobot \
|
--community-id lerobot \
|
||||||
--revision v1.2 \
|
|
||||||
--dry-run 1 \
|
--dry-run 1 \
|
||||||
--save-to-disk 1 \
|
--save-to-disk 1 \
|
||||||
--save-tests-to-disk 0 \
|
--save-tests-to-disk 0 \
|
||||||
|
|
@ -227,8 +223,7 @@ def push_dataset_to_hub(
|
||||||
test_hf_dataset = test_hf_dataset.with_format(None)
|
test_hf_dataset = test_hf_dataset.with_format(None)
|
||||||
test_hf_dataset.save_to_disk(str(tests_out_dir / "train"))
|
test_hf_dataset.save_to_disk(str(tests_out_dir / "train"))
|
||||||
|
|
||||||
# copy meta data to tests directory
|
save_meta_data(info, stats, episode_data_index, tests_meta_data_dir)
|
||||||
shutil.copytree(meta_data_dir, tests_meta_data_dir)
|
|
||||||
|
|
||||||
# copy videos of first episode to tests directory
|
# copy videos of first episode to tests directory
|
||||||
episode_index = 0
|
episode_index = 0
|
||||||
|
|
@ -237,6 +232,10 @@ def push_dataset_to_hub(
|
||||||
fname = f"{key}_episode_{episode_index:06d}.mp4"
|
fname = f"{key}_episode_{episode_index:06d}.mp4"
|
||||||
shutil.copy(videos_dir / fname, tests_videos_dir / fname)
|
shutil.copy(videos_dir / fname, tests_videos_dir / fname)
|
||||||
|
|
||||||
|
if not save_to_disk and out_dir.exists():
|
||||||
|
# remove possible temporary files remaining in the output directory
|
||||||
|
shutil.rmtree(out_dir)
|
||||||
|
|
||||||
|
|
||||||
def main():
|
def main():
|
||||||
parser = argparse.ArgumentParser()
|
parser = argparse.ArgumentParser()
|
||||||
|
|
@ -314,7 +313,7 @@ def main():
|
||||||
parser.add_argument(
|
parser.add_argument(
|
||||||
"--num-workers",
|
"--num-workers",
|
||||||
type=int,
|
type=int,
|
||||||
default=16,
|
default=8,
|
||||||
help="Number of processes of Dataloader for computing the dataset statistics.",
|
help="Number of processes of Dataloader for computing the dataset statistics.",
|
||||||
)
|
)
|
||||||
parser.add_argument(
|
parser.add_argument(
|
||||||
|
|
|
||||||
|
|
@ -15,15 +15,14 @@
|
||||||
# limitations under the License.
|
# limitations under the License.
|
||||||
import logging
|
import logging
|
||||||
import time
|
import time
|
||||||
|
from contextlib import nullcontext
|
||||||
from copy import deepcopy
|
from copy import deepcopy
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
|
|
||||||
import datasets
|
|
||||||
import hydra
|
import hydra
|
||||||
import torch
|
import torch
|
||||||
from datasets import concatenate_datasets
|
|
||||||
from datasets.utils import disable_progress_bars, enable_progress_bars
|
|
||||||
from omegaconf import DictConfig
|
from omegaconf import DictConfig
|
||||||
|
from torch.cuda.amp import GradScaler
|
||||||
|
|
||||||
from lerobot.common.datasets.factory import make_dataset
|
from lerobot.common.datasets.factory import make_dataset
|
||||||
from lerobot.common.datasets.utils import cycle
|
from lerobot.common.datasets.utils import cycle
|
||||||
|
|
@ -31,6 +30,7 @@ from lerobot.common.envs.factory import make_env
|
||||||
from lerobot.common.logger import Logger, log_output_dir
|
from lerobot.common.logger import Logger, log_output_dir
|
||||||
from lerobot.common.policies.factory import make_policy
|
from lerobot.common.policies.factory import make_policy
|
||||||
from lerobot.common.policies.policy_protocol import PolicyWithUpdate
|
from lerobot.common.policies.policy_protocol import PolicyWithUpdate
|
||||||
|
from lerobot.common.policies.utils import get_device_from_parameters
|
||||||
from lerobot.common.utils.utils import (
|
from lerobot.common.utils.utils import (
|
||||||
format_big_number,
|
format_big_number,
|
||||||
get_safe_torch_device,
|
get_safe_torch_device,
|
||||||
|
|
@ -69,7 +69,6 @@ def make_optimizer_and_scheduler(cfg, policy):
|
||||||
cfg.training.adam_eps,
|
cfg.training.adam_eps,
|
||||||
cfg.training.adam_weight_decay,
|
cfg.training.adam_weight_decay,
|
||||||
)
|
)
|
||||||
assert cfg.training.online_steps == 0, "Diffusion Policy does not handle online training."
|
|
||||||
from diffusers.optimization import get_scheduler
|
from diffusers.optimization import get_scheduler
|
||||||
|
|
||||||
lr_scheduler = get_scheduler(
|
lr_scheduler = get_scheduler(
|
||||||
|
|
@ -87,21 +86,40 @@ def make_optimizer_and_scheduler(cfg, policy):
|
||||||
return optimizer, lr_scheduler
|
return optimizer, lr_scheduler
|
||||||
|
|
||||||
|
|
||||||
def update_policy(policy, batch, optimizer, grad_clip_norm, lr_scheduler=None):
|
def update_policy(
|
||||||
|
policy,
|
||||||
|
batch,
|
||||||
|
optimizer,
|
||||||
|
grad_clip_norm,
|
||||||
|
grad_scaler: GradScaler,
|
||||||
|
lr_scheduler=None,
|
||||||
|
use_amp: bool = False,
|
||||||
|
):
|
||||||
"""Returns a dictionary of items for logging."""
|
"""Returns a dictionary of items for logging."""
|
||||||
start_time = time.time()
|
start_time = time.perf_counter()
|
||||||
|
device = get_device_from_parameters(policy)
|
||||||
policy.train()
|
policy.train()
|
||||||
|
with torch.autocast(device_type=device.type) if use_amp else nullcontext():
|
||||||
output_dict = policy.forward(batch)
|
output_dict = policy.forward(batch)
|
||||||
# TODO(rcadene): policy.unnormalize_outputs(out_dict)
|
# TODO(rcadene): policy.unnormalize_outputs(out_dict)
|
||||||
loss = output_dict["loss"]
|
loss = output_dict["loss"]
|
||||||
loss.backward()
|
grad_scaler.scale(loss).backward()
|
||||||
|
|
||||||
|
# Unscale the graident of the optimzer's assigned params in-place **prior to gradient clipping**.
|
||||||
|
grad_scaler.unscale_(optimizer)
|
||||||
|
|
||||||
grad_norm = torch.nn.utils.clip_grad_norm_(
|
grad_norm = torch.nn.utils.clip_grad_norm_(
|
||||||
policy.parameters(),
|
policy.parameters(),
|
||||||
grad_clip_norm,
|
grad_clip_norm,
|
||||||
error_if_nonfinite=False,
|
error_if_nonfinite=False,
|
||||||
)
|
)
|
||||||
|
|
||||||
optimizer.step()
|
# Optimizer's gradients are already unscaled, so scaler.step does not unscale them,
|
||||||
|
# although it still skips optimizer.step() if the gradients contain infs or NaNs.
|
||||||
|
grad_scaler.step(optimizer)
|
||||||
|
# Updates the scale for next iteration.
|
||||||
|
grad_scaler.update()
|
||||||
|
|
||||||
optimizer.zero_grad()
|
optimizer.zero_grad()
|
||||||
|
|
||||||
if lr_scheduler is not None:
|
if lr_scheduler is not None:
|
||||||
|
|
@ -115,7 +133,7 @@ def update_policy(policy, batch, optimizer, grad_clip_norm, lr_scheduler=None):
|
||||||
"loss": loss.item(),
|
"loss": loss.item(),
|
||||||
"grad_norm": float(grad_norm),
|
"grad_norm": float(grad_norm),
|
||||||
"lr": optimizer.param_groups[0]["lr"],
|
"lr": optimizer.param_groups[0]["lr"],
|
||||||
"update_s": time.time() - start_time,
|
"update_s": time.perf_counter() - start_time,
|
||||||
**{k: v for k, v in output_dict.items() if k != "loss"},
|
**{k: v for k, v in output_dict.items() if k != "loss"},
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
@ -211,103 +229,6 @@ def log_eval_info(logger, info, step, cfg, dataset, is_offline):
|
||||||
logger.log_dict(info, step, mode="eval")
|
logger.log_dict(info, step, mode="eval")
|
||||||
|
|
||||||
|
|
||||||
def calculate_online_sample_weight(n_off: int, n_on: int, pc_on: float):
|
|
||||||
"""
|
|
||||||
Calculate the sampling weight to be assigned to samples so that a specified percentage of the batch comes from online dataset (on average).
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- n_off (int): Number of offline samples, each with a sampling weight of 1.
|
|
||||||
- n_on (int): Number of online samples.
|
|
||||||
- pc_on (float): Desired percentage of online samples in decimal form (e.g., 50% as 0.5).
|
|
||||||
|
|
||||||
The total weight of offline samples is n_off * 1.0.
|
|
||||||
The total weight of offline samples is n_on * w.
|
|
||||||
The total combined weight of all samples is n_off + n_on * w.
|
|
||||||
The fraction of the weight that is online is n_on * w / (n_off + n_on * w).
|
|
||||||
We want this fraction to equal pc_on, so we set up the equation n_on * w / (n_off + n_on * w) = pc_on.
|
|
||||||
The solution is w = - (n_off * pc_on) / (n_on * (pc_on - 1))
|
|
||||||
"""
|
|
||||||
assert 0.0 <= pc_on <= 1.0
|
|
||||||
return -(n_off * pc_on) / (n_on * (pc_on - 1))
|
|
||||||
|
|
||||||
|
|
||||||
def add_episodes_inplace(
|
|
||||||
online_dataset: torch.utils.data.Dataset,
|
|
||||||
concat_dataset: torch.utils.data.ConcatDataset,
|
|
||||||
sampler: torch.utils.data.WeightedRandomSampler,
|
|
||||||
hf_dataset: datasets.Dataset,
|
|
||||||
episode_data_index: dict[str, torch.Tensor],
|
|
||||||
pc_online_samples: float,
|
|
||||||
):
|
|
||||||
"""
|
|
||||||
Modifies the online_dataset, concat_dataset, and sampler in place by integrating
|
|
||||||
new episodes from hf_dataset into the online_dataset, updating the concatenated
|
|
||||||
dataset's structure and adjusting the sampling strategy based on the specified
|
|
||||||
percentage of online samples.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- online_dataset (torch.utils.data.Dataset): The existing online dataset to be updated.
|
|
||||||
- concat_dataset (torch.utils.data.ConcatDataset): The concatenated dataset that combines
|
|
||||||
offline and online datasets, used for sampling purposes.
|
|
||||||
- sampler (torch.utils.data.WeightedRandomSampler): A sampler that will be updated to
|
|
||||||
reflect changes in the dataset sizes and specified sampling weights.
|
|
||||||
- hf_dataset (datasets.Dataset): A Hugging Face dataset containing the new episodes to be added.
|
|
||||||
- episode_data_index (dict): A dictionary containing two keys ("from" and "to") associated to dataset indices.
|
|
||||||
They indicate the start index and end index of each episode in the dataset.
|
|
||||||
- pc_online_samples (float): The target percentage of samples that should come from
|
|
||||||
the online dataset during sampling operations.
|
|
||||||
|
|
||||||
Raises:
|
|
||||||
- AssertionError: If the first episode_id or index in hf_dataset is not 0
|
|
||||||
"""
|
|
||||||
first_episode_idx = hf_dataset.select_columns("episode_index")[0]["episode_index"].item()
|
|
||||||
last_episode_idx = hf_dataset.select_columns("episode_index")[-1]["episode_index"].item()
|
|
||||||
first_index = hf_dataset.select_columns("index")[0]["index"].item()
|
|
||||||
last_index = hf_dataset.select_columns("index")[-1]["index"].item()
|
|
||||||
# sanity check
|
|
||||||
assert first_episode_idx == 0, f"{first_episode_idx=} is not 0"
|
|
||||||
assert first_index == 0, f"{first_index=} is not 0"
|
|
||||||
assert first_index == episode_data_index["from"][first_episode_idx].item()
|
|
||||||
assert last_index == episode_data_index["to"][last_episode_idx].item() - 1
|
|
||||||
|
|
||||||
if len(online_dataset) == 0:
|
|
||||||
# initialize online dataset
|
|
||||||
online_dataset.hf_dataset = hf_dataset
|
|
||||||
online_dataset.episode_data_index = episode_data_index
|
|
||||||
else:
|
|
||||||
# get the starting indices of the new episodes and frames to be added
|
|
||||||
start_episode_idx = last_episode_idx + 1
|
|
||||||
start_index = last_index + 1
|
|
||||||
|
|
||||||
def shift_indices(episode_index, index):
|
|
||||||
# note: we dont shift "frame_index" since it represents the index of the frame in the episode it belongs to
|
|
||||||
example = {"episode_index": episode_index + start_episode_idx, "index": index + start_index}
|
|
||||||
return example
|
|
||||||
|
|
||||||
disable_progress_bars() # map has a tqdm progress bar
|
|
||||||
hf_dataset = hf_dataset.map(shift_indices, input_columns=["episode_index", "index"])
|
|
||||||
enable_progress_bars()
|
|
||||||
|
|
||||||
episode_data_index["from"] += start_index
|
|
||||||
episode_data_index["to"] += start_index
|
|
||||||
|
|
||||||
# extend online dataset
|
|
||||||
online_dataset.hf_dataset = concatenate_datasets([online_dataset.hf_dataset, hf_dataset])
|
|
||||||
|
|
||||||
# update the concatenated dataset length used during sampling
|
|
||||||
concat_dataset.cumulative_sizes = concat_dataset.cumsum(concat_dataset.datasets)
|
|
||||||
|
|
||||||
# update the sampling weights for each frame so that online frames get sampled a certain percentage of times
|
|
||||||
len_online = len(online_dataset)
|
|
||||||
len_offline = len(concat_dataset) - len_online
|
|
||||||
weight_offline = 1.0
|
|
||||||
weight_online = calculate_online_sample_weight(len_offline, len_online, pc_online_samples)
|
|
||||||
sampler.weights = torch.tensor([weight_offline] * len_offline + [weight_online] * len(online_dataset))
|
|
||||||
|
|
||||||
# update the total number of samples used during sampling
|
|
||||||
sampler.num_samples = len(concat_dataset)
|
|
||||||
|
|
||||||
|
|
||||||
def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = None):
|
def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = None):
|
||||||
if out_dir is None:
|
if out_dir is None:
|
||||||
raise NotImplementedError()
|
raise NotImplementedError()
|
||||||
|
|
@ -316,11 +237,11 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
|
||||||
|
|
||||||
init_logging()
|
init_logging()
|
||||||
|
|
||||||
if cfg.training.online_steps > 0 and cfg.eval.batch_size > 1:
|
if cfg.training.online_steps > 0:
|
||||||
logging.warning("eval.batch_size > 1 not supported for online training steps")
|
raise NotImplementedError("Online training is not implemented yet.")
|
||||||
|
|
||||||
# Check device is available
|
# Check device is available
|
||||||
get_safe_torch_device(cfg.device, log=True)
|
device = get_safe_torch_device(cfg.device, log=True)
|
||||||
|
|
||||||
torch.backends.cudnn.benchmark = True
|
torch.backends.cudnn.benchmark = True
|
||||||
torch.backends.cuda.matmul.allow_tf32 = True
|
torch.backends.cuda.matmul.allow_tf32 = True
|
||||||
|
|
@ -338,6 +259,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
|
||||||
# Create optimizer and scheduler
|
# Create optimizer and scheduler
|
||||||
# Temporary hack to move optimizer out of policy
|
# Temporary hack to move optimizer out of policy
|
||||||
optimizer, lr_scheduler = make_optimizer_and_scheduler(cfg, policy)
|
optimizer, lr_scheduler = make_optimizer_and_scheduler(cfg, policy)
|
||||||
|
grad_scaler = GradScaler(enabled=cfg.use_amp)
|
||||||
|
|
||||||
num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad)
|
num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad)
|
||||||
num_total_params = sum(p.numel() for p in policy.parameters())
|
num_total_params = sum(p.numel() for p in policy.parameters())
|
||||||
|
|
@ -358,6 +280,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
|
||||||
def evaluate_and_checkpoint_if_needed(step):
|
def evaluate_and_checkpoint_if_needed(step):
|
||||||
if step % cfg.training.eval_freq == 0:
|
if step % cfg.training.eval_freq == 0:
|
||||||
logging.info(f"Eval policy at step {step}")
|
logging.info(f"Eval policy at step {step}")
|
||||||
|
with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.use_amp else nullcontext():
|
||||||
eval_info = eval_policy(
|
eval_info = eval_policy(
|
||||||
eval_env,
|
eval_env,
|
||||||
policy,
|
policy,
|
||||||
|
|
@ -389,23 +312,30 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
|
||||||
num_workers=4,
|
num_workers=4,
|
||||||
batch_size=cfg.training.batch_size,
|
batch_size=cfg.training.batch_size,
|
||||||
shuffle=True,
|
shuffle=True,
|
||||||
pin_memory=cfg.device != "cpu",
|
pin_memory=device.type != "cpu",
|
||||||
drop_last=False,
|
drop_last=False,
|
||||||
)
|
)
|
||||||
dl_iter = cycle(dataloader)
|
dl_iter = cycle(dataloader)
|
||||||
|
|
||||||
policy.train()
|
policy.train()
|
||||||
step = 0 # number of policy update (forward + backward + optim)
|
|
||||||
is_offline = True
|
is_offline = True
|
||||||
for offline_step in range(cfg.training.offline_steps):
|
for step in range(cfg.training.offline_steps):
|
||||||
if offline_step == 0:
|
if step == 0:
|
||||||
logging.info("Start offline training on a fixed dataset")
|
logging.info("Start offline training on a fixed dataset")
|
||||||
batch = next(dl_iter)
|
batch = next(dl_iter)
|
||||||
|
|
||||||
for key in batch:
|
for key in batch:
|
||||||
batch[key] = batch[key].to(cfg.device, non_blocking=True)
|
batch[key] = batch[key].to(device, non_blocking=True)
|
||||||
|
|
||||||
train_info = update_policy(policy, batch, optimizer, cfg.training.grad_clip_norm, lr_scheduler)
|
train_info = update_policy(
|
||||||
|
policy,
|
||||||
|
batch,
|
||||||
|
optimizer,
|
||||||
|
cfg.training.grad_clip_norm,
|
||||||
|
grad_scaler=grad_scaler,
|
||||||
|
lr_scheduler=lr_scheduler,
|
||||||
|
use_amp=cfg.use_amp,
|
||||||
|
)
|
||||||
|
|
||||||
# TODO(rcadene): is it ok if step_t=0 = 0 and not 1 as previously done?
|
# TODO(rcadene): is it ok if step_t=0 = 0 and not 1 as previously done?
|
||||||
if step % cfg.training.log_freq == 0:
|
if step % cfg.training.log_freq == 0:
|
||||||
|
|
@ -415,11 +345,6 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
|
||||||
# so we pass in step + 1.
|
# so we pass in step + 1.
|
||||||
evaluate_and_checkpoint_if_needed(step + 1)
|
evaluate_and_checkpoint_if_needed(step + 1)
|
||||||
|
|
||||||
step += 1
|
|
||||||
|
|
||||||
# create an env dedicated to online episodes collection from policy rollout
|
|
||||||
online_training_env = make_env(cfg, n_envs=1)
|
|
||||||
|
|
||||||
# create an empty online dataset similar to offline dataset
|
# create an empty online dataset similar to offline dataset
|
||||||
online_dataset = deepcopy(offline_dataset)
|
online_dataset = deepcopy(offline_dataset)
|
||||||
online_dataset.hf_dataset = {}
|
online_dataset.hf_dataset = {}
|
||||||
|
|
@ -436,58 +361,11 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
|
||||||
num_workers=4,
|
num_workers=4,
|
||||||
batch_size=cfg.training.batch_size,
|
batch_size=cfg.training.batch_size,
|
||||||
sampler=sampler,
|
sampler=sampler,
|
||||||
pin_memory=cfg.device != "cpu",
|
pin_memory=device.type != "cpu",
|
||||||
drop_last=False,
|
drop_last=False,
|
||||||
)
|
)
|
||||||
dl_iter = cycle(dataloader)
|
|
||||||
|
|
||||||
online_step = 0
|
|
||||||
is_offline = False
|
|
||||||
for env_step in range(cfg.training.online_steps):
|
|
||||||
if env_step == 0:
|
|
||||||
logging.info("Start online training by interacting with environment")
|
|
||||||
|
|
||||||
policy.eval()
|
|
||||||
with torch.no_grad():
|
|
||||||
eval_info = eval_policy(
|
|
||||||
online_training_env,
|
|
||||||
policy,
|
|
||||||
n_episodes=1,
|
|
||||||
return_episode_data=True,
|
|
||||||
start_seed=cfg.training.online_env_seed,
|
|
||||||
enable_progbar=True,
|
|
||||||
)
|
|
||||||
|
|
||||||
add_episodes_inplace(
|
|
||||||
online_dataset,
|
|
||||||
concat_dataset,
|
|
||||||
sampler,
|
|
||||||
hf_dataset=eval_info["episodes"]["hf_dataset"],
|
|
||||||
episode_data_index=eval_info["episodes"]["episode_data_index"],
|
|
||||||
pc_online_samples=cfg.training.online_sampling_ratio,
|
|
||||||
)
|
|
||||||
|
|
||||||
policy.train()
|
|
||||||
for _ in range(cfg.training.online_steps_between_rollouts):
|
|
||||||
batch = next(dl_iter)
|
|
||||||
|
|
||||||
for key in batch:
|
|
||||||
batch[key] = batch[key].to(cfg.device, non_blocking=True)
|
|
||||||
|
|
||||||
train_info = update_policy(policy, batch, optimizer, cfg.training.grad_clip_norm, lr_scheduler)
|
|
||||||
|
|
||||||
if step % cfg.training.log_freq == 0:
|
|
||||||
log_train_info(logger, train_info, step, cfg, online_dataset, is_offline)
|
|
||||||
|
|
||||||
# Note: evaluate_and_checkpoint_if_needed happens **after** the `step`th training update has completed,
|
|
||||||
# so we pass in step + 1.
|
|
||||||
evaluate_and_checkpoint_if_needed(step + 1)
|
|
||||||
|
|
||||||
step += 1
|
|
||||||
online_step += 1
|
|
||||||
|
|
||||||
eval_env.close()
|
eval_env.close()
|
||||||
online_training_env.close()
|
|
||||||
logging.info("End of training")
|
logging.info("End of training")
|
||||||
|
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -404,28 +404,28 @@ files = [
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "cmake"
|
name = "cmake"
|
||||||
version = "3.29.2"
|
version = "3.29.3"
|
||||||
description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software"
|
description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software"
|
||||||
optional = false
|
optional = false
|
||||||
python-versions = ">=3.7"
|
python-versions = ">=3.7"
|
||||||
files = [
|
files = [
|
||||||
{file = "cmake-3.29.2-py3-none-macosx_10_10_universal2.macosx_10_10_x86_64.macosx_11_0_arm64.macosx_11_0_universal2.whl", hash = "sha256:1d40c5451d6467b20a0a6015a5a6b6dc86f61b83f71f935740485b259100a34e"},
|
{file = "cmake-3.29.3-py3-none-macosx_10_10_universal2.macosx_10_10_x86_64.macosx_11_0_arm64.macosx_11_0_universal2.whl", hash = "sha256:355f515826023338094514a2181724e297ed2145bc0792dacaa9ed3772b98733"},
|
||||||
{file = "cmake-3.29.2-py3-none-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:ed3108e646cd65a4e23fa1cbe8123569a29334a3f2a8ce214d871406b161bedb"},
|
{file = "cmake-3.29.3-py3-none-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:ab5eb91e7f5bbfc2f0e23c964c3a3e74c6e6a26e9b59b57b87192d249b1b7162"},
|
||||||
{file = "cmake-3.29.2-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:40aafe612b03a9fa140cca4024ba60b74cd92372f3f349d8062cba1f021e5001"},
|
{file = "cmake-3.29.3-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:ae9e5dcd77822f89e042ad820ef25a52327bb0d15fd7a492ad4886edb31fae52"},
|
||||||
{file = "cmake-3.29.2-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:027eebe9bb74c31759581a543f27bc1828fc76e6fc45b2b48b51f27847904669"},
|
{file = "cmake-3.29.3-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b09d1f0f46a880fdfc50374917fd4c850d9428b244535343bb5411658a36e202"},
|
||||||
{file = "cmake-3.29.2-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f1f087985fc2460476b0901716fbddb2fd69b7fe7bf1350e1ab5dc508d22600e"},
|
{file = "cmake-3.29.3-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8d05cf16a6fb370cc344b3552ab321524cba1f067da240876c09cab571bf6ec0"},
|
||||||
{file = "cmake-3.29.2-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:df2c63ce6d504aa4c91a42fd22d3887065ab029569691deb56ec19d0decd0ae9"},
|
{file = "cmake-3.29.3-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6c0a23fbb3daeecdc42d233c1a2df233714c2db59e75ab154e2af469c1c308a5"},
|
||||||
{file = "cmake-3.29.2-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6ea5ce007893d7d1363e13433dde1c0c7c344372213a90ff3c56e896a335301d"},
|
{file = "cmake-3.29.3-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1037218e135302f396eca444e24ca892d8a440589f1a859313e06484f10c350f"},
|
||||||
{file = "cmake-3.29.2-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f9e941e73202cfa667ee488d1d88b8a758b516dcfa2a2728e73dbdcbfbdebf57"},
|
{file = "cmake-3.29.3-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c84eead2ea6f596fe5ac58beedbfc9bc1f460c410c481348b3783b4794f4b1a2"},
|
||||||
{file = "cmake-3.29.2-py3-none-musllinux_1_1_aarch64.whl", hash = "sha256:37222e23485338c72b7ea51f865d8c6847d519f7e2222922fb70b4896ca6e897"},
|
{file = "cmake-3.29.3-py3-none-musllinux_1_1_aarch64.whl", hash = "sha256:e1fd53ca2f24dc0aad54934c2472cb83e273b94b4bad23fcdbd438515881f5a7"},
|
||||||
{file = "cmake-3.29.2-py3-none-musllinux_1_1_i686.whl", hash = "sha256:eeed08932c748647488280dc97ac00bcfeae5d760451105200cfe66c52ce6468"},
|
{file = "cmake-3.29.3-py3-none-musllinux_1_1_i686.whl", hash = "sha256:00225a2be8422d4b6f2ad2da10d7dfe2ad844748bd1defa94f236bfabb0d2d44"},
|
||||||
{file = "cmake-3.29.2-py3-none-musllinux_1_1_ppc64le.whl", hash = "sha256:db7a05df020ba67bacd3070dd1645c76ca96fabd06d6aaa63288fd845706e47a"},
|
{file = "cmake-3.29.3-py3-none-musllinux_1_1_ppc64le.whl", hash = "sha256:28fe371f1865943118a0f669af87344c799751f85a5be084197c006ee6329d89"},
|
||||||
{file = "cmake-3.29.2-py3-none-musllinux_1_1_s390x.whl", hash = "sha256:83b35de822ddabaaa184a7d8f9827381350c42d627689c775b214347f57c9e41"},
|
{file = "cmake-3.29.3-py3-none-musllinux_1_1_s390x.whl", hash = "sha256:ad184528fa9560bf4167279e8e4e7168a5fa1cc87a9f0b4b99ffbc79588b0cf9"},
|
||||||
{file = "cmake-3.29.2-py3-none-musllinux_1_1_x86_64.whl", hash = "sha256:cc0e36752e581430a93e58a268e515bb4ec1373b9e9911571f2cac1d2a6b5bec"},
|
{file = "cmake-3.29.3-py3-none-musllinux_1_1_x86_64.whl", hash = "sha256:40cd0ec1310e52fa29b4e2b07829d56ae95f01ea0b2479ece359259849269f86"},
|
||||||
{file = "cmake-3.29.2-py3-none-win32.whl", hash = "sha256:a941e26fba81cf74832c8a0e17e007452e05b6ad4941b3d2d18c75faa4a677d8"},
|
{file = "cmake-3.29.3-py3-none-win32.whl", hash = "sha256:a2c15ab9e4922d71d98a6495a5fd661dd00b3d4ada79a3d183f996fff45db011"},
|
||||||
{file = "cmake-3.29.2-py3-none-win_amd64.whl", hash = "sha256:23336c8ca01205d18d92ed8de6c54e570c352a58e378b7f9adc02ef00f433960"},
|
{file = "cmake-3.29.3-py3-none-win_amd64.whl", hash = "sha256:dd8aaffe5d8dc2dd41421dc63c39b64df30a7109392e276e2b6d021805b770e9"},
|
||||||
{file = "cmake-3.29.2-py3-none-win_arm64.whl", hash = "sha256:e722a949f7c91084dba61f8f17a9854787182ab711ed0b84b1507b24a8e12e25"},
|
{file = "cmake-3.29.3-py3-none-win_arm64.whl", hash = "sha256:6672a873855e9a8f954390d0352c1d09b034a36b5f4cc5da012ae292f28623f7"},
|
||||||
{file = "cmake-3.29.2.tar.gz", hash = "sha256:6a4c1185cb2eca7263190a5754d0c9edf738d9e50bff464f78f48d0c05318e7c"},
|
{file = "cmake-3.29.3.tar.gz", hash = "sha256:d04adb1a8b878e92a734742cb0db9c59e3828abcf8ec9c930eb8a01faa00c9df"},
|
||||||
]
|
]
|
||||||
|
|
||||||
[package.extras]
|
[package.extras]
|
||||||
|
|
@ -956,13 +956,13 @@ tqdm = ["tqdm"]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "gdown"
|
name = "gdown"
|
||||||
version = "5.1.0"
|
version = "5.2.0"
|
||||||
description = "Google Drive Public File/Folder Downloader"
|
description = "Google Drive Public File/Folder Downloader"
|
||||||
optional = false
|
optional = false
|
||||||
python-versions = ">=3.8"
|
python-versions = ">=3.8"
|
||||||
files = [
|
files = [
|
||||||
{file = "gdown-5.1.0-py3-none-any.whl", hash = "sha256:421530fd238fa15d41ba43219a79fdc28efe8ac11022173abad333701b77de2c"},
|
{file = "gdown-5.2.0-py3-none-any.whl", hash = "sha256:33083832d82b1101bdd0e9df3edd0fbc0e1c5f14c9d8c38d2a35bf1683b526d6"},
|
||||||
{file = "gdown-5.1.0.tar.gz", hash = "sha256:550a72dc5ca2819fe4bcc15d80d05d7c98c0b90e57256254b77d0256b9df4683"},
|
{file = "gdown-5.2.0.tar.gz", hash = "sha256:2145165062d85520a3cd98b356c9ed522c5e7984d408535409fd46f94defc787"},
|
||||||
]
|
]
|
||||||
|
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
|
|
@ -972,7 +972,7 @@ requests = {version = "*", extras = ["socks"]}
|
||||||
tqdm = "*"
|
tqdm = "*"
|
||||||
|
|
||||||
[package.extras]
|
[package.extras]
|
||||||
test = ["build", "mypy", "pytest", "pytest-xdist", "ruff", "twine", "types-requests"]
|
test = ["build", "mypy", "pytest", "pytest-xdist", "ruff", "twine", "types-requests", "types-setuptools"]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "gitdb"
|
name = "gitdb"
|
||||||
|
|
@ -1050,13 +1050,13 @@ test = ["pytest (>=8.1.0)", "pytest-cov (>=5.0.0)"]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "gym-pusht"
|
name = "gym-pusht"
|
||||||
version = "0.1.3"
|
version = "0.1.4"
|
||||||
description = "A gymnasium environment for PushT."
|
description = "A gymnasium environment for PushT."
|
||||||
optional = true
|
optional = true
|
||||||
python-versions = "<4.0,>=3.10"
|
python-versions = "<4.0,>=3.10"
|
||||||
files = [
|
files = [
|
||||||
{file = "gym_pusht-0.1.3-py3-none-any.whl", hash = "sha256:feeb02493a03d1aacc45d43d6397962c50ed779ab7e4019d73af11d2f0b3831b"},
|
{file = "gym_pusht-0.1.4-py3-none-any.whl", hash = "sha256:04ec628fac80d77cf4edf280ed528ab06f7b8997e4452c78999b4730c2d269ab"},
|
||||||
{file = "gym_pusht-0.1.3.tar.gz", hash = "sha256:c8e9a5256035ba49841ebbc7c32a06c4fa2daa52f5fad80da941b607c4553e28"},
|
{file = "gym_pusht-0.1.4.tar.gz", hash = "sha256:5926ef8098bde7f6a180bdd34b4ce3a827fb333847cc54d8ad47c430e249f44c"},
|
||||||
]
|
]
|
||||||
|
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
|
|
@ -1177,6 +1177,78 @@ files = [
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
numpy = ">=1.17.3"
|
numpy = ">=1.17.3"
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "hf-transfer"
|
||||||
|
version = "0.1.6"
|
||||||
|
description = ""
|
||||||
|
optional = false
|
||||||
|
python-versions = ">=3.7"
|
||||||
|
files = [
|
||||||
|
{file = "hf_transfer-0.1.6-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:6fd3d61f9229d27def007e53540412507b74ac2fdb1a29985ae0b6a5137749a2"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b043bb78df1225de043eb041de9d97783fcca14a0bdc1b1d560fc172fc21b648"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7db60dd18eae4fa6ea157235fb82196cde5313995b396d1b591aad3b790a7f8f"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:30d31dbab9b5a558cce407b8728e39d87d7af1ef8745ddb90187e9ae0b9e1e90"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f6b368bddd757efc7af3126ba81f9ac8f9435e2cc00902cb3d64f2be28d8f719"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:aa2086d8aefaaa3e144e167324574882004c0cec49bf2d0638ec4b74732d8da0"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:45d8985a0940bfe1535cb4ca781f5c11e47c83798ef3373ee1f5d57bbe527a9c"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2f42b89735f1cde22f2a795d1f0915741023235666be7de45879e533c7d6010c"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp310-none-win32.whl", hash = "sha256:2d2c4c4613f3ad45b6ce6291e347b2d3ba1b86816635681436567e461cb3c961"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp310-none-win_amd64.whl", hash = "sha256:78b0eed8d8dce60168a46e584b9742b816af127d7e410a713e12c31249195342"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:f1d8c172153f9a6cdaecf137612c42796076f61f6bea1072c90ac2e17c1ab6fa"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2c601996351f90c514a75a0eeb02bf700b1ad1db2d946cbfe4b60b79e29f0b2f"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8e585c808405557d3f5488f385706abb696997bbae262ea04520757e30836d9d"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ec51af1e8cf4268c268bd88932ade3d7ca895a3c661b42493503f02610ae906b"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d106fdf996332f6df3ed3fab6d6332df82e8c1fb4b20fd81a491ca4d2ab5616a"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e9c2ee9e9fde5a0319cc0e8ddfea10897482bc06d5709b10a238f1bc2ebcbc0b"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f394ea32bc7802b061e549d3133efc523b4ae4fd19bf4b74b183ca6066eef94e"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4282f09902114cd67fca98a1a1bad569a44521a8395fedf327e966714f68b977"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp311-none-win32.whl", hash = "sha256:276dbf307d5ab6f1bcbf57b5918bfcf9c59d6848ccb28242349e1bb5985f983b"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp311-none-win_amd64.whl", hash = "sha256:fa475175c51451186bea804471995fa8e7b2a48a61dcca55534911dc25955527"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:23d157a67acfa00007799323a1c441b2bbacc7dee625b016b7946fe0e25e6c89"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6067342a2864b988f861cd2d31bd78eb1e84d153a3f6df38485b6696d9ad3013"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:91cfcb3070e205b58fa8dc8bcb6a62ccc40913fcdb9cd1ff7c364c8e3aa85345"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:eb76064ac5165d5eeaaf8d0903e8bf55477221ecc2a4a4d69f0baca065ab905b"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9dabd3a177d83028f164984cf4dd859f77ec1e20c97a6f307ff8fcada0785ef1"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d0bf4254e44f64a26e0a5b73b5d7e8d91bb36870718fb4f8e126ec943ff4c805"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5d32c1b106f38f336ceb21531f4db9b57d777b9a33017dafdb6a5316388ebe50"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ff05aba3c83921e5c7635ba9f07c693cc893350c447644824043aeac27b285f5"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp312-none-win32.whl", hash = "sha256:051ef0c55607652cb5974f59638da035773254b9a07d7ee5b574fe062de4c9d1"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp312-none-win_amd64.whl", hash = "sha256:716fb5c574fcbdd8092ce73f9b6c66f42e3544337490f77c60ec07df02bd081b"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6c0c981134a55965e279cb7be778c1ccaf93f902fc9ebe31da4f30caf824cc4d"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp37-cp37m-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:1ef1f145f04c5b573915bcb1eb5db4039c74f6b46fce73fc473c4287e613b623"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d0a7609b004db3347dbb7796df45403eceb171238210d054d93897d6d84c63a4"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:60f0864bf5996773dbd5f8ae4d1649041f773fe9d5769f4c0eeb5553100acef3"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5d01e55d630ffe70a4f5d0ed576a04c6a48d7c65ca9a7d18f2fca385f20685a9"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d855946c5062b665190de15b2bdbd4c8eddfee35350bfb7564592e23d36fbbd3"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp37-none-win32.whl", hash = "sha256:fd40b2409cfaf3e8aba20169ee09552f69140e029adeec261b988903ff0c8f6f"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp37-none-win_amd64.whl", hash = "sha256:0e0eba49d46d3b5481919aea0794aec625fbc6ecdf13fe7e0e9f3fc5d5ad5971"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7e669fecb29fc454449739f9f53ed9253197e7c19e6a6eaa0f08334207af4287"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:89f701802892e5eb84f89f402686861f87dc227d6082b05f4e9d9b4e8015a3c3"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b6f2b0c8b95b01409275d789a9b74d5f2e146346f985d384bf50ec727caf1ccc"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:aa855a2fa262792a230f9efcdb5da6d431b747d1861d2a69fe7834b19aea077e"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4aa8ca349afb2f0713475426946261eb2035e4efb50ebd2c1d5ad04f395f4217"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:01255f043996bc7d1bae62d8afc5033a90c7e36ce308b988eeb84afe0a69562f"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp38-none-win32.whl", hash = "sha256:60b1db183e8a7540cd4f8b2160ff4de55f77cb0c3fc6a10be1e7c30eb1b2bdeb"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp38-none-win_amd64.whl", hash = "sha256:fb8be3cba6aaa50ab2e9dffbd25c8eb2046785eeff642cf0cdd0dd9ae6be3539"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d09af35e3e3f09b664e6429e9a0dc200f29c5bdfd88bdd9666de51183b1fe202"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:a4505bd707cc14d85c800f961fad8ca76f804a8ad22fbb7b1a217d8d0c15e6a5"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2c453fd8b0be9740faa23cecd1f28ee9ead7d900cefa64ff836960c503a744c9"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:13cb8884e718a78c3b81a8cdec9c7ac196dd42961fce55c3ccff3dd783e5ad7a"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:39cd39df171a2b5404de69c4e6cd14eee47f6fe91c1692f939bfb9e59a0110d8"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8ff0629ee9f98df57a783599602eb498f9ec3619dc69348b12e4d9d754abf0e9"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp39-none-win32.whl", hash = "sha256:164a6ce445eb0cc7c645f5b6e1042c003d33292520c90052b6325f30c98e4c5f"},
|
||||||
|
{file = "hf_transfer-0.1.6-cp39-none-win_amd64.whl", hash = "sha256:11b8b4b73bf455f13218c5f827698a30ae10998ca31b8264b51052868c7a9f11"},
|
||||||
|
{file = "hf_transfer-0.1.6-pp310-pypy310_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:16957ba057376a99ea361074ce1094f61b58e769defa6be2422ae59c0b6a6530"},
|
||||||
|
{file = "hf_transfer-0.1.6-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7db952112e3b8ee1a5cbf500d2443e9ce4fb893281c5310a3e31469898628005"},
|
||||||
|
{file = "hf_transfer-0.1.6-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d39d826a7344f5e39f438d62632acd00467aa54a083b66496f61ef67a9885a56"},
|
||||||
|
{file = "hf_transfer-0.1.6-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a4e2653fbfa92e7651db73d99b697c8684e7345c479bd6857da80bed6138abb2"},
|
||||||
|
{file = "hf_transfer-0.1.6-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:144277e6a86add10b90ec3b583253aec777130312256bfc8d5ade5377e253807"},
|
||||||
|
{file = "hf_transfer-0.1.6-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3bb53bcd16365313b2aa0dbdc28206f577d70770f31249cdabc387ac5841edcc"},
|
||||||
|
{file = "hf_transfer-0.1.6-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:990d73a5a68d8261980f146c51f4c5f9995314011cb225222021ad7c39f3af2d"},
|
||||||
|
{file = "hf_transfer-0.1.6-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:652406037029ab9b4097b4c5f29321bad5f64c2b46fbff142509d918aec87c29"},
|
||||||
|
{file = "hf_transfer-0.1.6.tar.gz", hash = "sha256:deb505a7d417d7055fd7b3549eadb91dfe782941261f3344025c486c16d1d2f9"},
|
||||||
|
]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "huggingface-hub"
|
name = "huggingface-hub"
|
||||||
version = "0.23.0"
|
version = "0.23.0"
|
||||||
|
|
@ -1191,6 +1263,7 @@ files = [
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
filelock = "*"
|
filelock = "*"
|
||||||
fsspec = ">=2023.5.0"
|
fsspec = ">=2023.5.0"
|
||||||
|
hf-transfer = {version = ">=0.1.4", optional = true, markers = "extra == \"hf-transfer\""}
|
||||||
packaging = ">=20.9"
|
packaging = ">=20.9"
|
||||||
pyyaml = ">=5.1"
|
pyyaml = ">=5.1"
|
||||||
requests = "*"
|
requests = "*"
|
||||||
|
|
@ -1503,165 +1576,149 @@ files = [
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "lxml"
|
name = "lxml"
|
||||||
version = "5.2.1"
|
version = "5.2.2"
|
||||||
description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
|
description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
|
||||||
optional = true
|
optional = true
|
||||||
python-versions = ">=3.6"
|
python-versions = ">=3.6"
|
||||||
files = [
|
files = [
|
||||||
{file = "lxml-5.2.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:1f7785f4f789fdb522729ae465adcaa099e2a3441519df750ebdccc481d961a1"},
|
{file = "lxml-5.2.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:364d03207f3e603922d0d3932ef363d55bbf48e3647395765f9bfcbdf6d23632"},
|
||||||
{file = "lxml-5.2.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:6cc6ee342fb7fa2471bd9b6d6fdfc78925a697bf5c2bcd0a302e98b0d35bfad3"},
|
{file = "lxml-5.2.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:50127c186f191b8917ea2fb8b206fbebe87fd414a6084d15568c27d0a21d60db"},
|
||||||
{file = "lxml-5.2.1-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:794f04eec78f1d0e35d9e0c36cbbb22e42d370dda1609fb03bcd7aeb458c6377"},
|
{file = "lxml-5.2.2-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:74e4f025ef3db1c6da4460dd27c118d8cd136d0391da4e387a15e48e5c975147"},
|
||||||
{file = "lxml-5.2.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c817d420c60a5183953c783b0547d9eb43b7b344a2c46f69513d5952a78cddf3"},
|
{file = "lxml-5.2.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:981a06a3076997adf7c743dcd0d7a0415582661e2517c7d961493572e909aa1d"},
|
||||||
{file = "lxml-5.2.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2213afee476546a7f37c7a9b4ad4d74b1e112a6fafffc9185d6d21f043128c81"},
|
{file = "lxml-5.2.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:aef5474d913d3b05e613906ba4090433c515e13ea49c837aca18bde190853dff"},
|
||||||
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||||||
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||||||
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|
||||||
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|
||||||
]
|
]
|
||||||
|
|
||||||
[package.extras]
|
[package.extras]
|
||||||
|
|
@ -2491,13 +2548,13 @@ xmp = ["defusedxml"]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "platformdirs"
|
name = "platformdirs"
|
||||||
version = "4.2.1"
|
version = "4.2.2"
|
||||||
description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
|
description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
|
||||||
optional = false
|
optional = false
|
||||||
python-versions = ">=3.8"
|
python-versions = ">=3.8"
|
||||||
files = [
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files = [
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||||||
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||||||
]
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]
|
||||||
|
|
||||||
[package.extras]
|
[package.extras]
|
||||||
|
|
@ -2602,47 +2659,47 @@ test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
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||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "pyarrow"
|
name = "pyarrow"
|
||||||
version = "16.0.0"
|
version = "16.1.0"
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||||||
description = "Python library for Apache Arrow"
|
description = "Python library for Apache Arrow"
|
||||||
optional = false
|
optional = false
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||||||
python-versions = ">=3.8"
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python-versions = ">=3.8"
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||||||
]
|
]
|
||||||
|
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
|
|
@ -2972,7 +3029,6 @@ files = [
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||||||
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|
|
@ -3009,90 +3065,90 @@ files = [
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||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "regex"
|
name = "regex"
|
||||||
version = "2024.5.10"
|
version = "2024.5.15"
|
||||||
description = "Alternative regular expression module, to replace re."
|
description = "Alternative regular expression module, to replace re."
|
||||||
optional = false
|
optional = false
|
||||||
python-versions = ">=3.8"
|
python-versions = ">=3.8"
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||||||
files = [
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files = [
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{file = "regex-2024.5.15.tar.gz", hash = "sha256:d3ee02d9e5f482cc8309134a91eeaacbdd2261ba111b0fef3748eeb4913e6a2c"},
|
||||||
]
|
]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
|
|
@ -3119,22 +3175,22 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "rerun-sdk"
|
name = "rerun-sdk"
|
||||||
version = "0.15.1"
|
version = "0.16.0"
|
||||||
description = "The Rerun Logging SDK"
|
description = "The Rerun Logging SDK"
|
||||||
optional = false
|
optional = false
|
||||||
python-versions = "<3.13,>=3.8"
|
python-versions = "<3.13,>=3.8"
|
||||||
files = [
|
files = [
|
||||||
{file = "rerun_sdk-0.15.1-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:be8f4e55c53bd9734bd0b8e91a9765daeb55e56caddc1bacdb358d12121daaa0"},
|
{file = "rerun_sdk-0.16.0-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:1cc6dc66d089e296f945dc238301889efb61dd6d338b5d00f76981cf7aed0a74"},
|
||||||
{file = "rerun_sdk-0.15.1-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:e039ed441b6dcd5939e20f0f67fef4ffd54645777574822f48cd6f636efa3756"},
|
{file = "rerun_sdk-0.16.0-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:faf231897655e46eb975695df2b0ace07db362d697e697f9a3dff52f81c0dc5d"},
|
||||||
{file = "rerun_sdk-0.15.1-cp38-abi3-manylinux_2_31_aarch64.whl", hash = "sha256:5c067ba1c3304a0bb74bd33df8f7145ce7d405c823bfc8709396bbdd672a759e"},
|
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|
||||||
{file = "rerun_sdk-0.15.1-cp38-abi3-manylinux_2_31_x86_64.whl", hash = "sha256:e8a96fff6e0c184a59b433430f5f87c96895e4b69dc0e43abb56a0e0737edc35"},
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{file = "rerun_sdk-0.16.0-cp38-abi3-manylinux_2_31_x86_64.whl", hash = "sha256:5b8d1476f73a3ad1a5d3f21b61c633f3ab62aa80fa0b049f5ad10bf1227681ab"},
|
||||||
{file = "rerun_sdk-0.15.1-cp38-abi3-win_amd64.whl", hash = "sha256:377a888e0cbe06835f376cd160ab322e9935ebd1317384381856236bd4347950"},
|
{file = "rerun_sdk-0.16.0-cp38-abi3-win_amd64.whl", hash = "sha256:aff0051a263b8c3067243c0126d319845baf4fe640899f04aeef7daf151f35e4"},
|
||||||
]
|
]
|
||||||
|
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
attrs = ">=23.1.0"
|
attrs = ">=23.1.0"
|
||||||
numpy = ">=1.23,<2"
|
numpy = ">=1.23,<2"
|
||||||
pillow = "*"
|
pillow = ">=8.0.0"
|
||||||
pyarrow = ">=14.0.2"
|
pyarrow = ">=14.0.2"
|
||||||
typing-extensions = ">=4.5"
|
typing-extensions = ">=4.5"
|
||||||
|
|
||||||
|
|
@ -3350,13 +3406,13 @@ test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "po
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "sentry-sdk"
|
name = "sentry-sdk"
|
||||||
version = "2.1.1"
|
version = "2.2.0"
|
||||||
description = "Python client for Sentry (https://sentry.io)"
|
description = "Python client for Sentry (https://sentry.io)"
|
||||||
optional = false
|
optional = false
|
||||||
python-versions = ">=3.6"
|
python-versions = ">=3.6"
|
||||||
files = [
|
files = [
|
||||||
{file = "sentry_sdk-2.1.1-py2.py3-none-any.whl", hash = "sha256:99aeb78fb76771513bd3b2829d12613130152620768d00cd3e45ac00cb17950f"},
|
{file = "sentry_sdk-2.2.0-py2.py3-none-any.whl", hash = "sha256:674f58da37835ea7447fe0e34c57b4a4277fad558b0a7cb4a6c83bcb263086be"},
|
||||||
{file = "sentry_sdk-2.1.1.tar.gz", hash = "sha256:95d8c0bb41c8b0bc37ab202c2c4a295bb84398ee05f4cdce55051cd75b926ec1"},
|
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|
||||||
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|
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|
||||||
|
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
|
|
@ -3848,13 +3904,13 @@ zstd = ["zstandard (>=0.18.0)"]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "virtualenv"
|
name = "virtualenv"
|
||||||
version = "20.26.1"
|
version = "20.26.2"
|
||||||
description = "Virtual Python Environment builder"
|
description = "Virtual Python Environment builder"
|
||||||
optional = true
|
optional = true
|
||||||
python-versions = ">=3.7"
|
python-versions = ">=3.7"
|
||||||
files = [
|
files = [
|
||||||
{file = "virtualenv-20.26.1-py3-none-any.whl", hash = "sha256:7aa9982a728ae5892558bff6a2839c00b9ed145523ece2274fad6f414690ae75"},
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|
||||||
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|
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|
||||||
]
|
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|
||||||
|
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
|
|
@ -4130,13 +4186,13 @@ multidict = ">=4.0"
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "zarr"
|
name = "zarr"
|
||||||
version = "2.18.0"
|
version = "2.18.1"
|
||||||
description = "An implementation of chunked, compressed, N-dimensional arrays for Python"
|
description = "An implementation of chunked, compressed, N-dimensional arrays for Python"
|
||||||
optional = false
|
optional = false
|
||||||
python-versions = ">=3.9"
|
python-versions = ">=3.9"
|
||||||
files = [
|
files = [
|
||||||
{file = "zarr-2.18.0-py3-none-any.whl", hash = "sha256:7f8532b6a3f50f22e809e130e09353637ec8b5bb5e95a5a0bfaae91f63978b5d"},
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|
||||||
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|
||||||
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|
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|
||||||
|
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
|
|
@ -4151,18 +4207,18 @@ jupyter = ["ipytree (>=0.2.2)", "ipywidgets (>=8.0.0)", "notebook"]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "zipp"
|
name = "zipp"
|
||||||
version = "3.18.1"
|
version = "3.18.2"
|
||||||
description = "Backport of pathlib-compatible object wrapper for zip files"
|
description = "Backport of pathlib-compatible object wrapper for zip files"
|
||||||
optional = false
|
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|
||||||
python-versions = ">=3.8"
|
python-versions = ">=3.8"
|
||||||
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|
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|
||||||
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||||||
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|
||||||
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|
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|
||||||
|
|
||||||
[package.extras]
|
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|
||||||
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
|
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
|
||||||
testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
|
testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
|
||||||
|
|
||||||
[extras]
|
[extras]
|
||||||
aloha = ["gym-aloha"]
|
aloha = ["gym-aloha"]
|
||||||
|
|
@ -4175,4 +4231,4 @@ xarm = ["gym-xarm"]
|
||||||
[metadata]
|
[metadata]
|
||||||
lock-version = "2.0"
|
lock-version = "2.0"
|
||||||
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|
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|
||||||
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||||||
|
|
|
||||||
|
|
@ -41,9 +41,9 @@ numba = ">=0.59.0"
|
||||||
torch = "^2.2.1"
|
torch = "^2.2.1"
|
||||||
opencv-python = ">=4.9.0"
|
opencv-python = ">=4.9.0"
|
||||||
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|
diffusers = "^0.27.2"
|
||||||
torchvision = ">=0.18.0"
|
torchvision = ">=0.17.1"
|
||||||
h5py = ">=3.10.0"
|
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|
||||||
huggingface-hub = ">=0.21.4"
|
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|
||||||
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|
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|
||||||
cmake = ">=3.29.0.1"
|
cmake = ">=3.29.0.1"
|
||||||
gym-pusht = { version = ">=0.1.3", optional = true}
|
gym-pusht = { version = ">=0.1.3", optional = true}
|
||||||
|
|
|
||||||
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Reference in New Issue