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lerobot
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Merge remote-tracking branch 'upstream/main' into tutorial_act_pusht

This commit is contained in:
Alexander Soare 2024-05-21 13:00:45 +01:00
parent 45c6a67677 b6c216b590
commit 24409025c8
604 changed files with 2098 additions and 969 deletions
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4
.gitattributes vendored
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@ -1,2 +1,6 @@
*.memmap 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

6
.github/workflows/test.yml vendored
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@ -29,6 +29,8 @@ jobs:
MUJOCO_GL: egl
steps:
- uses: actions/checkout@v4
with:
lfs: true # Ensure LFS files are pulled
- name: Install EGL
run: sudo apt-get update && sudo apt-get install -y libegl1-mesa-dev
@ -65,6 +67,8 @@ jobs:
MUJOCO_GL: egl
steps:
- uses: actions/checkout@v4
with:
lfs: true # Ensure LFS files are pulled
- name: Install poetry
run: |
@ -97,6 +101,8 @@ jobs:
MUJOCO_GL: egl
steps:
- uses: actions/checkout@v4
with:
lfs: true # Ensure LFS files are pulled
- name: Install EGL
run: sudo apt-get update && sudo apt-get install -y libegl1-mesa-dev

5
CONTRIBUTING.md
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@ -195,6 +195,11 @@ Follow these steps to start contributing:
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/).
It is a good idea to sync your copy of the code with the original

38
Makefile
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@ -20,6 +20,8 @@ build-gpu:
test-end-to-end:
${MAKE} test-act-ete-train
${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-eval
${MAKE} test-tdmpc-ete-train
@ -30,6 +32,7 @@ test-end-to-end:
test-act-ete-train:
python lerobot/scripts/train.py \
policy=act \
policy.dim_model=64 \
env=aloha \
wandb.enable=False \
training.offline_steps=2 \
@ -52,9 +55,40 @@ test-act-ete-eval:
env.episode_length=8 \
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:
python lerobot/scripts/train.py \
policy=diffusion \
policy.down_dims=\[64,128,256\] \
policy.diffusion_step_embed_dim=32 \
policy.num_inference_steps=10 \
env=pusht \
wandb.enable=False \
training.offline_steps=2 \
@ -75,6 +109,7 @@ test-diffusion-ete-eval:
env.episode_length=8 \
device=cpu \
# TODO(alexander-soare): Restore online_steps to 2 when it is reinstated.
test-tdmpc-ete-train:
python lerobot/scripts/train.py \
policy=tdmpc \
@ -83,7 +118,7 @@ test-tdmpc-ete-train:
dataset_repo_id=lerobot/xarm_lift_medium \
wandb.enable=False \
training.offline_steps=2 \
training.online_steps=2 \
training.online_steps=0 \
eval.n_episodes=1 \
eval.batch_size=1 \
env.episode_length=2 \
@ -101,7 +136,6 @@ test-tdmpc-ete-eval:
env.episode_length=8 \
device=cpu \
test-default-ete-eval:
python lerobot/scripts/eval.py \
--config lerobot/configs/default.yaml \

4
README.md
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@ -198,11 +198,11 @@ 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
```
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
python lerobot/scripts/push_dataset_to_hub.py \
--data-dir data \
--dataset-id aloha_ping_ping \
--dataset-id aloha_static_pingpong_test \
--raw-format aloha_hdf5 \
--community-id lerobot
```

2
docker/lerobot-gpu/Dockerfile
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@ -8,7 +8,7 @@ ARG DEBIAN_FRONTEND=noninteractive
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential cmake \
git git-lfs openssh-client \
nano vim \
nano vim ffmpeg \
htop atop nvtop \
sed gawk grep curl wget \
tcpdump sysstat screen \

90
examples/4_calculate_validation_loss.py Normal file
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@ -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}")

10
lerobot/__init__.py
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@ -61,13 +61,21 @@ available_datasets_per_env = {
"lerobot/aloha_sim_insertion_scripted",
"lerobot/aloha_sim_transfer_cube_human",
"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": [
"lerobot/xarm_lift_medium",
"lerobot/xarm_lift_medium_replay",
"lerobot/xarm_push_medium",
"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",
],
}

10
lerobot/common/datasets/lerobot_dataset.py
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@ -20,17 +20,19 @@ import datasets
import torch
from lerobot.common.datasets.utils import (
calculate_episode_data_index,
load_episode_data_index,
load_hf_dataset,
load_info,
load_previous_and_future_frames,
load_stats,
load_videos,
reset_episode_index,
)
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
CODEBASE_VERSION = "v1.3"
CODEBASE_VERSION = "v1.4"
class LeRobotDataset(torch.utils.data.Dataset):
@ -54,7 +56,11 @@ class LeRobotDataset(torch.utils.data.Dataset):
# TODO(rcadene, aliberts): implement faster transfer
# https://huggingface.co/docs/huggingface_hub/en/guides/download#faster-downloads
self.hf_dataset = load_hf_dataset(repo_id, version, root, split)
self.episode_data_index = load_episode_data_index(repo_id, version, root)
if split == "train":
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.info = load_info(repo_id, version, root)
if self.video:

58
lerobot/common/datasets/push_dataset_to_hub/_download_raw.py
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@ -24,17 +24,16 @@ import shutil
from pathlib import Path
import tqdm
ALOHA_RAW_URLS_DIR = "lerobot/common/datasets/push_dataset_to_hub/_aloha_raw_urls"
from huggingface_hub import snapshot_download
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)
elif "xarm" in dataset_id:
download_xarm(raw_dir)
elif "aloha" in dataset_id:
download_aloha(raw_dir, dataset_id)
elif "umi" in dataset_id:
download_umi(raw_dir)
else:
@ -103,37 +102,13 @@ def download_xarm(raw_dir: Path):
zip_path.unlink()
def download_aloha(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}'."
def download_hub(raw_dir: Path, dataset_id: str):
raw_dir = Path(raw_dir)
raw_dir.mkdir(parents=True, exist_ok=True)
logging.info(f"Start downloading from google drive for {dataset_id}")
for url in urls:
if "drive.google.com/drive/folders" in url:
# 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}")
logging.info(f"Start downloading from huggingface.co/cadene for {dataset_id}")
snapshot_download(f"cadene/{dataset_id}_raw", repo_type="dataset", local_dir=raw_dir)
logging.info(f"Finish downloading from huggingface.co/cadene for {dataset_id}")
def download_umi(raw_dir: Path):
@ -148,21 +123,30 @@ def download_umi(raw_dir: Path):
if __name__ == "__main__":
data_dir = Path("data")
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",
"xarm_lift_medium",
"xarm_lift_medium_replay",
"xarm_push_medium",
"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_chair",
"aloha_mobile_elevator",
"aloha_mobile_shrimp",
"aloha_mobile_wash_pan",
"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_candy",
"aloha_static_coffee",

24
lerobot/common/datasets/push_dataset_to_hub/aloha_hdf5_format.py
View File

@ -17,7 +17,7 @@
Contains utilities to process raw data format of HDF5 files like in: https://github.com/tonyzhaozh/act
"""
import re
import gc
import shutil
from pathlib import Path
@ -79,10 +79,8 @@ def load_from_raw(raw_dir, out_dir, fps, video, debug):
episode_data_index = {"from": [], "to": []}
id_from = 0
for ep_path in tqdm.tqdm(hdf5_files, total=len(hdf5_files)):
for ep_idx, ep_path in tqdm.tqdm(enumerate(hdf5_files), total=len(hdf5_files)):
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]
# 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"][:])
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 = {}
@ -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["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["episode_index"] = torch.tensor([ep_idx] * num_frames)
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
gc.collect()
# process first episode only
if debug:
break
@ -167,6 +175,14 @@ def to_hf_dataset(data_dict, video) -> Dataset:
features["observation.state"] = Sequence(
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(
length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)
)

98
lerobot/common/datasets/utils.py
View File

@ -14,7 +14,9 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import re
from pathlib import Path
from typing import Dict
import datasets
import torch
@ -79,7 +81,23 @@ def hf_transform_to_torch(items_dict):
def load_hf_dataset(repo_id, version, root, split) -> datasets.Dataset:
"""hf_dataset contains all the observations, states, actions, rewards, etc."""
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:
hf_dataset = load_dataset(repo_id, revision=version, split=split)
hf_dataset.set_transform(hf_transform_to_torch)
@ -245,6 +263,84 @@ def load_previous_and_future_frames(
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):
"""The equivalent of itertools.cycle, but safe for Pytorch dataloaders.

4
lerobot/configs/default.yaml
View File

@ -10,6 +10,9 @@ hydra:
name: default
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)
# AND for the evaluation environments.
seed: ???
@ -17,6 +20,7 @@ dataset_repo_id: lerobot/pusht
training:
offline_steps: ???
# NOTE: `online_steps` is not implemented yet. It's here as a placeholder.
online_steps: ???
online_steps_between_rollouts: ???
online_sampling_ratio: 0.5

3
lerobot/configs/policy/tdmpc.yaml
View File

@ -5,7 +5,8 @@ dataset_repo_id: lerobot/xarm_lift_medium
training:
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
online_steps_between_rollouts: 1
online_sampling_ratio: 0.5

24
lerobot/scripts/eval.py
View File

@ -46,6 +46,7 @@ import json
import logging
import threading
import time
from contextlib import nullcontext
from copy import deepcopy
from datetime import datetime as dt
from pathlib import Path
@ -520,7 +521,7 @@ def eval(
raise NotImplementedError()
# 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.cuda.matmul.allow_tf32 = True
@ -539,16 +540,17 @@ def eval(
policy = make_policy(hydra_cfg=hydra_cfg, dataset_stats=make_dataset(hydra_cfg).stats)
policy.eval()
info = eval_policy(
env,
policy,
hydra_cfg.eval.n_episodes,
max_episodes_rendered=10,
video_dir=Path(out_dir) / "eval",
start_seed=hydra_cfg.seed,
enable_progbar=True,
enable_inner_progbar=True,
)
with torch.no_grad(), torch.autocast(device_type=device.type) if hydra_cfg.use_amp else nullcontext():
info = eval_policy(
env,
policy,
hydra_cfg.eval.n_episodes,
max_episodes_rendered=10,
video_dir=Path(out_dir) / "eval",
start_seed=hydra_cfg.seed,
enable_progbar=True,
enable_inner_progbar=True,
)
print(info["aggregated"])
# Save info

13
lerobot/scripts/push_dataset_to_hub.py
View File

@ -25,7 +25,6 @@ python lerobot/scripts/push_dataset_to_hub.py \
--dataset-id pusht \
--raw-format pusht_zarr \
--community-id lerobot \
--revision v1.2 \
--dry-run 1 \
--save-to-disk 1 \
--save-tests-to-disk 0 \
@ -36,7 +35,6 @@ python lerobot/scripts/push_dataset_to_hub.py \
--dataset-id xarm_lift_medium \
--raw-format xarm_pkl \
--community-id lerobot \
--revision v1.2 \
--dry-run 1 \
--save-to-disk 1 \
--save-tests-to-disk 0 \
@ -47,7 +45,6 @@ python lerobot/scripts/push_dataset_to_hub.py \
--dataset-id aloha_sim_insertion_scripted \
--raw-format aloha_hdf5 \
--community-id lerobot \
--revision v1.2 \
--dry-run 1 \
--save-to-disk 1 \
--save-tests-to-disk 0 \
@ -58,7 +55,6 @@ python lerobot/scripts/push_dataset_to_hub.py \
--dataset-id umi_cup_in_the_wild \
--raw-format umi_zarr \
--community-id lerobot \
--revision v1.2 \
--dry-run 1 \
--save-to-disk 1 \
--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.save_to_disk(str(tests_out_dir / "train"))
# copy meta data to tests directory
shutil.copytree(meta_data_dir, tests_meta_data_dir)
save_meta_data(info, stats, episode_data_index, tests_meta_data_dir)
# copy videos of first episode to tests directory
episode_index = 0
@ -237,6 +232,10 @@ def push_dataset_to_hub(
fname = f"{key}_episode_{episode_index:06d}.mp4"
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():
parser = argparse.ArgumentParser()
@ -314,7 +313,7 @@ def main():
parser.add_argument(
"--num-workers",
type=int,
default=16,
default=8,
help="Number of processes of Dataloader for computing the dataset statistics.",
)
parser.add_argument(

236
lerobot/scripts/train.py
View File

@ -15,15 +15,14 @@
# limitations under the License.
import logging
import time
from contextlib import nullcontext
from copy import deepcopy
from pathlib import Path
import datasets
import hydra
import torch
from datasets import concatenate_datasets
from datasets.utils import disable_progress_bars, enable_progress_bars
from omegaconf import DictConfig
from torch.cuda.amp import GradScaler
from lerobot.common.datasets.factory import make_dataset
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.policies.factory import make_policy
from lerobot.common.policies.policy_protocol import PolicyWithUpdate
from lerobot.common.policies.utils import get_device_from_parameters
from lerobot.common.utils.utils import (
format_big_number,
get_safe_torch_device,
@ -69,7 +69,6 @@ def make_optimizer_and_scheduler(cfg, policy):
cfg.training.adam_eps,
cfg.training.adam_weight_decay,
)
assert cfg.training.online_steps == 0, "Diffusion Policy does not handle online training."
from diffusers.optimization import get_scheduler
lr_scheduler = get_scheduler(
@ -87,21 +86,40 @@ def make_optimizer_and_scheduler(cfg, policy):
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."""
start_time = time.time()
start_time = time.perf_counter()
device = get_device_from_parameters(policy)
policy.train()
output_dict = policy.forward(batch)
# TODO(rcadene): policy.unnormalize_outputs(out_dict)
loss = output_dict["loss"]
loss.backward()
with torch.autocast(device_type=device.type) if use_amp else nullcontext():
output_dict = policy.forward(batch)
# TODO(rcadene): policy.unnormalize_outputs(out_dict)
loss = output_dict["loss"]
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_(
policy.parameters(),
grad_clip_norm,
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()
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(),
"grad_norm": float(grad_norm),
"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"},
}
@ -211,103 +229,6 @@ def log_eval_info(logger, info, step, cfg, dataset, is_offline):
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):
if out_dir is None:
raise NotImplementedError()
@ -316,11 +237,11 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
init_logging()
if cfg.training.online_steps > 0 and cfg.eval.batch_size > 1:
logging.warning("eval.batch_size > 1 not supported for online training steps")
if cfg.training.online_steps > 0:
raise NotImplementedError("Online training is not implemented yet.")
# 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.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
# Temporary hack to move optimizer out of 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_total_params = sum(p.numel() for p in policy.parameters())
@ -358,14 +280,15 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
def evaluate_and_checkpoint_if_needed(step):
if step % cfg.training.eval_freq == 0:
logging.info(f"Eval policy at step {step}")
eval_info = eval_policy(
eval_env,
policy,
cfg.eval.n_episodes,
video_dir=Path(out_dir) / "eval",
max_episodes_rendered=4,
start_seed=cfg.seed,
)
with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.use_amp else nullcontext():
eval_info = eval_policy(
eval_env,
policy,
cfg.eval.n_episodes,
video_dir=Path(out_dir) / "eval",
max_episodes_rendered=4,
start_seed=cfg.seed,
)
log_eval_info(logger, eval_info["aggregated"], step, cfg, offline_dataset, is_offline)
if cfg.wandb.enable:
logger.log_video(eval_info["video_paths"][0], step, mode="eval")
@ -389,23 +312,30 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
num_workers=4,
batch_size=cfg.training.batch_size,
shuffle=True,
pin_memory=cfg.device != "cpu",
pin_memory=device.type != "cpu",
drop_last=False,
)
dl_iter = cycle(dataloader)
policy.train()
step = 0 # number of policy update (forward + backward + optim)
is_offline = True
for offline_step in range(cfg.training.offline_steps):
if offline_step == 0:
for step in range(cfg.training.offline_steps):
if step == 0:
logging.info("Start offline training on a fixed dataset")
batch = next(dl_iter)
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?
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.
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
online_dataset = deepcopy(offline_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,
batch_size=cfg.training.batch_size,
sampler=sampler,
pin_memory=cfg.device != "cpu",
pin_memory=device.type != "cpu",
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()
online_training_env.close()
logging.info("End of training")

77
poetry.lock generated
View File

@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
[[package]]
name = "absl-py"
@ -1177,6 +1177,78 @@ files = [
[package.dependencies]
numpy = ">=1.17.3"
[[package]]
name = "hf-transfer"
version = "0.1.6"
description = ""
optional = false
python-versions = ">=3.7"
files = [
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