diff --git a/checkport.py b/checkport.py deleted file mode 100644 index 7f79af6f..00000000 --- a/checkport.py +++ /dev/null @@ -1,18 +0,0 @@ -import socket - - -def check_port(host, port): - s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - try: - s.connect((host, port)) - print(f"Connection successful to {host}:{port}!") - except Exception as e: - print(f"Connection failed to {host}:{port}: {e}") - finally: - s.close() - - -if __name__ == "__main__": - host = "127.0.0.1" # or "localhost" - port = 51350 - check_port(host, port) diff --git a/docker/lerobot-gpu-mani-skill/Dockerfile b/docker/lerobot-gpu-mani-skill/Dockerfile deleted file mode 100644 index e45d84e8..00000000 --- a/docker/lerobot-gpu-mani-skill/Dockerfile +++ /dev/null @@ -1,11 +0,0 @@ -FROM huggingface/lerobot-gpu:latest - -RUN apt-get update && apt-get install -y --no-install-recommends \ - libvulkan1 vulkan-tools \ - && apt-get clean && rm -rf /var/lib/apt/lists/* - -RUN pip install --upgrade --no-cache-dir pip -RUN pip install --no-cache-dir ".[mani-skill]" - -# Set EGL as the rendering backend for MuJoCo -ENV MUJOCO_GL="egl" diff --git a/examples/port_datasets/pusht_zarr.py b/examples/port_datasets/pusht_zarr.py deleted file mode 100644 index 6766ac83..00000000 --- a/examples/port_datasets/pusht_zarr.py +++ /dev/null @@ -1,228 +0,0 @@ -import shutil -from pathlib import Path - -import numpy as np -import torch - -from lerobot.common.datasets.lerobot_dataset import LEROBOT_HOME, LeRobotDataset -from lerobot.common.datasets.push_dataset_to_hub._download_raw import download_raw - -PUSHT_TASK = "Push the T-shaped blue block onto the T-shaped green target surface." -PUSHT_FEATURES = { - "observation.state": { - "dtype": "float32", - "shape": (2,), - "names": { - "axes": ["x", "y"], - }, - }, - "action": { - "dtype": "float32", - "shape": (2,), - "names": { - "axes": ["x", "y"], - }, - }, - "next.reward": { - "dtype": "float32", - "shape": (1,), - "names": None, - }, - "next.success": { - "dtype": "bool", - "shape": (1,), - "names": None, - }, - "observation.environment_state": { - "dtype": "float32", - "shape": (16,), - "names": [ - "keypoints", - ], - }, - "observation.image": { - "dtype": None, - "shape": (3, 96, 96), - "names": [ - "channel", - "height", - "width", - ], - }, -} - - -def build_features(mode: str) -> dict: - features = PUSHT_FEATURES - if mode == "keypoints": - features.pop("observation.image") - else: - features.pop("observation.environment_state") - features["observation.image"]["dtype"] = mode - - return features - - -def load_raw_dataset(zarr_path: Path): - try: - from lerobot.common.datasets.push_dataset_to_hub._diffusion_policy_replay_buffer import ( - ReplayBuffer as DiffusionPolicyReplayBuffer, - ) - except ModuleNotFoundError as e: - print( - "`gym_pusht` is not installed. Please install it with `pip install 'lerobot[gym_pusht]'`" - ) - raise e - - zarr_data = DiffusionPolicyReplayBuffer.copy_from_path(zarr_path) - return zarr_data - - -def calculate_coverage(zarr_data): - try: - import pymunk - from gym_pusht.envs.pusht import PushTEnv, pymunk_to_shapely - except ModuleNotFoundError as e: - print( - "`gym_pusht` is not installed. Please install it with `pip install 'lerobot[gym_pusht]'`" - ) - raise e - - block_pos = zarr_data["state"][:, 2:4] - block_angle = zarr_data["state"][:, 4] - - num_frames = len(block_pos) - - coverage = np.zeros((num_frames,)) - # 8 keypoints with 2 coords each - keypoints = np.zeros((num_frames, 16)) - - # Set x, y, theta (in radians) - goal_pos_angle = np.array([256, 256, np.pi / 4]) - goal_body = PushTEnv.get_goal_pose_body(goal_pos_angle) - - for i in range(num_frames): - space = pymunk.Space() - space.gravity = 0, 0 - space.damping = 0 - - # Add walls. - walls = [ - PushTEnv.add_segment(space, (5, 506), (5, 5), 2), - PushTEnv.add_segment(space, (5, 5), (506, 5), 2), - PushTEnv.add_segment(space, (506, 5), (506, 506), 2), - PushTEnv.add_segment(space, (5, 506), (506, 506), 2), - ] - space.add(*walls) - - block_body, block_shapes = PushTEnv.add_tee( - space, block_pos[i].tolist(), block_angle[i].item() - ) - goal_geom = pymunk_to_shapely(goal_body, block_body.shapes) - block_geom = pymunk_to_shapely(block_body, block_body.shapes) - intersection_area = goal_geom.intersection(block_geom).area - goal_area = goal_geom.area - coverage[i] = intersection_area / goal_area - keypoints[i] = torch.from_numpy(PushTEnv.get_keypoints(block_shapes).flatten()) - - return coverage, keypoints - - -def calculate_success(coverage: float, success_threshold: float): - return coverage > success_threshold - - -def calculate_reward(coverage: float, success_threshold: float): - return np.clip(coverage / success_threshold, 0, 1) - - -def main(raw_dir: Path, repo_id: str, mode: str = "video", push_to_hub: bool = True): - if mode not in ["video", "image", "keypoints"]: - raise ValueError(mode) - - if (LEROBOT_HOME / repo_id).exists(): - shutil.rmtree(LEROBOT_HOME / repo_id) - - if not raw_dir.exists(): - download_raw(raw_dir, repo_id="lerobot-raw/pusht_raw") - - zarr_data = load_raw_dataset(zarr_path=raw_dir / "pusht_cchi_v7_replay.zarr") - - env_state = zarr_data["state"][:] - agent_pos = env_state[:, :2] - - action = zarr_data["action"][:] - image = zarr_data["img"] # (b, h, w, c) - - episode_data_index = { - "from": np.concatenate(([0], zarr_data.meta["episode_ends"][:-1])), - "to": zarr_data.meta["episode_ends"], - } - - # Calculate success and reward based on the overlapping area - # of the T-object and the T-area. - coverage, keypoints = calculate_coverage(zarr_data) - success = calculate_success(coverage, success_threshold=0.95) - reward = calculate_reward(coverage, success_threshold=0.95) - - features = build_features(mode) - dataset = LeRobotDataset.create( - repo_id=repo_id, - fps=10, - robot_type="2d pointer", - features=features, - image_writer_threads=4, - ) - episodes = range(len(episode_data_index["from"])) - for ep_idx in episodes: - from_idx = episode_data_index["from"][ep_idx] - to_idx = episode_data_index["to"][ep_idx] - num_frames = to_idx - from_idx - - for frame_idx in range(num_frames): - i = from_idx + frame_idx - frame = { - "action": torch.from_numpy(action[i]), - # Shift reward and success by +1 until the last item of the episode - "next.reward": reward[i + (frame_idx < num_frames - 1)], - "next.success": success[i + (frame_idx < num_frames - 1)], - } - - frame["observation.state"] = torch.from_numpy(agent_pos[i]) - - if mode == "keypoints": - frame["observation.environment_state"] = torch.from_numpy(keypoints[i]) - else: - frame["observation.image"] = torch.from_numpy(image[i]) - - dataset.add_frame(frame) - - dataset.save_episode(task=PUSHT_TASK) - - dataset.consolidate() - - if push_to_hub: - dataset.push_to_hub() - - -if __name__ == "__main__": - # To try this script, modify the repo id with your own HuggingFace user (e.g cadene/pusht) - repo_id = "lerobot/pusht" - - modes = ["video", "image", "keypoints"] - # Uncomment if you want to try with a specific mode - # modes = ["video"] - # modes = ["image"] - # modes = ["keypoints"] - - raw_dir = Path("data/lerobot-raw/pusht_raw") - for mode in modes: - if mode in ["image", "keypoints"]: - repo_id += f"_{mode}" - - # download and load raw dataset, create LeRobotDataset, populate it, push to hub - main(raw_dir, repo_id=repo_id, mode=mode) - - # Uncomment if you want to load the local dataset and explore it - # dataset = LeRobotDataset(repo_id=repo_id, local_files_only=True) - # breakpoint() diff --git a/lerobot/common/logger.py b/lerobot/common/logger.py deleted file mode 100644 index 84038a41..00000000 --- a/lerobot/common/logger.py +++ /dev/null @@ -1,329 +0,0 @@ -#!/usr/bin/env python - -# Copyright 2024 The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Borrowed from https://github.com/fyhMer/fowm/blob/main/src/logger.py - -# TODO(rcadene, alexander-soare): clean this file -""" - -import logging -import os -import re -from glob import glob -from pathlib import Path - -import torch -import wandb -from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE -from omegaconf import DictConfig, OmegaConf -from termcolor import colored -from torch.optim import Optimizer -from torch.optim.lr_scheduler import LRScheduler - -from lerobot.common.policies.policy_protocol import Policy -from lerobot.common.utils.utils import get_global_random_state, set_global_random_state - - -def log_output_dir(out_dir): - logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {out_dir}") - - -def cfg_to_group(cfg: DictConfig, return_list: bool = False) -> list[str] | str: - """Return a group name for logging. Optionally returns group name as list.""" - lst = [ - f"policy:{cfg.policy.name}", - f"dataset:{cfg.dataset_repo_id}", - f"env:{cfg.env.name}", - f"seed:{cfg.seed}", - ] - return lst if return_list else "-".join(lst) - - -def get_wandb_run_id_from_filesystem(checkpoint_dir: Path) -> str: - # Get the WandB run ID. - paths = glob(str(checkpoint_dir / "../wandb/latest-run/run-*")) - if len(paths) != 1: - raise RuntimeError("Couldn't get the previous WandB run ID for run resumption.") - match = re.search(r"run-([^\.]+).wandb", paths[0].split("/")[-1]) - if match is None: - raise RuntimeError("Couldn't get the previous WandB run ID for run resumption.") - wandb_run_id = match.groups(0)[0] - return wandb_run_id - - -class Logger: - """Primary logger object. Logs either locally or using wandb. - - The logger creates the following directory structure: - - provided_log_dir - ├── .hydra # hydra's configuration cache - ├── checkpoints - │ ├── specific_checkpoint_name - │ │ ├── pretrained_model # Hugging Face pretrained model directory - │ │ │ ├── ... - │ │ └── training_state.pth # optimizer, scheduler, and random states + training step - | ├── another_specific_checkpoint_name - │ │ ├── ... - | ├── ... - │ └── last # a softlink to the last logged checkpoint - """ - - pretrained_model_dir_name = "pretrained_model" - training_state_file_name = "training_state.pth" - - def __init__( - self, cfg: DictConfig, log_dir: str, wandb_job_name: str | None = None - ): - """ - Args: - log_dir: The directory to save all logs and training outputs to. - job_name: The WandB job name. - """ - self._cfg = cfg - self.log_dir = Path(log_dir) - self.log_dir.mkdir(parents=True, exist_ok=True) - self.checkpoints_dir = self.get_checkpoints_dir(log_dir) - self.last_checkpoint_dir = self.get_last_checkpoint_dir(log_dir) - self.last_pretrained_model_dir = self.get_last_pretrained_model_dir(log_dir) - - # Set up WandB. - self._group = cfg_to_group(cfg) - project = cfg.get("wandb", {}).get("project") - entity = cfg.get("wandb", {}).get("entity") - enable_wandb = cfg.get("wandb", {}).get("enable", False) - run_offline = not enable_wandb or not project - if run_offline: - logging.info( - colored("Logs will be saved locally.", "yellow", attrs=["bold"]) - ) - self._wandb = None - else: - os.environ["WANDB_SILENT"] = "true" - wandb_run_id = None - if cfg.resume: - wandb_run_id = get_wandb_run_id_from_filesystem(self.checkpoints_dir) - - wandb.init( - id=wandb_run_id, - project=project, - entity=entity, - name=wandb_job_name, - notes=cfg.get("wandb", {}).get("notes"), - tags=cfg_to_group(cfg, return_list=True), - dir=log_dir, - config=OmegaConf.to_container(cfg, resolve=True), - # TODO(rcadene): try set to True - save_code=False, - # TODO(rcadene): split train and eval, and run async eval with job_type="eval" - job_type="train_eval", - resume="must" if cfg.resume else None, - ) - # Handle custom step key for rl asynchronous training. - self._wandb_custom_step_key: set[str] | None = None - print(colored("Logs will be synced with wandb.", "blue", attrs=["bold"])) - logging.info( - f"Track this run --> {colored(wandb.run.get_url(), 'yellow', attrs=['bold'])}" - ) - self._wandb = wandb - - @classmethod - def get_checkpoints_dir(cls, log_dir: str | Path) -> Path: - """Given the log directory, get the sub-directory in which checkpoints will be saved.""" - return Path(log_dir) / "checkpoints" - - @classmethod - def get_last_checkpoint_dir(cls, log_dir: str | Path) -> Path: - """Given the log directory, get the sub-directory in which the last checkpoint will be saved.""" - return cls.get_checkpoints_dir(log_dir) / "last" - - @classmethod - def get_last_pretrained_model_dir(cls, log_dir: str | Path) -> Path: - """ - Given the log directory, get the sub-directory in which the last checkpoint's pretrained weights will - be saved. - """ - return cls.get_last_checkpoint_dir(log_dir) / cls.pretrained_model_dir_name - - def save_model( - self, save_dir: Path, policy: Policy, wandb_artifact_name: str | None = None - ): - """Save the weights of the Policy model using PyTorchModelHubMixin. - - The weights are saved in a folder called "pretrained_model" under the checkpoint directory. - - Optionally also upload the model to WandB. - """ - self.checkpoints_dir.mkdir(parents=True, exist_ok=True) - policy.save_pretrained(save_dir) - # Also save the full Hydra config for the env configuration. - OmegaConf.save(self._cfg, save_dir / "config.yaml") - if self._wandb and not self._cfg.wandb.disable_artifact: - # note wandb artifact does not accept ":" or "/" in its name - artifact = self._wandb.Artifact(wandb_artifact_name, type="model") - artifact.add_file(save_dir / SAFETENSORS_SINGLE_FILE) - self._wandb.log_artifact(artifact) - if self.last_checkpoint_dir.exists(): - os.remove(self.last_checkpoint_dir) - - def save_training_state( - self, - save_dir: Path, - train_step: int, - optimizer: Optimizer | dict, - scheduler: LRScheduler | None, - interaction_step: int | None = None, - ): - """Checkpoint the global training_step, optimizer state, scheduler state, and random state. - - All of these are saved as "training_state.pth" under the checkpoint directory. - """ - # In Sac, for example, we have a dictionary of torch.optim.Optimizer - if type(optimizer) is dict: - optimizer_state_dict = {} - for k in optimizer: - optimizer_state_dict[k] = optimizer[k].state_dict() - else: - optimizer_state_dict = optimizer.state_dict() - - training_state = { - "step": train_step, - "optimizer": optimizer_state_dict, - **get_global_random_state(), - } - # Interaction step is related to the distributed training code - # In that setup, we have two kinds of steps, the online step of the env and the optimization step - # We need to save both in order to resume the optimization properly and not break the logs dependant on the interaction step - if interaction_step is not None: - training_state["interaction_step"] = interaction_step - if scheduler is not None: - training_state["scheduler"] = scheduler.state_dict() - torch.save(training_state, save_dir / self.training_state_file_name) - - def save_checkpoint( - self, - train_step: int, - policy: Policy, - optimizer: Optimizer, - scheduler: LRScheduler | None, - identifier: str, - interaction_step: int | None = None, - ): - """Checkpoint the model weights and the training state.""" - checkpoint_dir = self.checkpoints_dir / str(identifier) - wandb_artifact_name = ( - None - if self._wandb is None - else f"{self._group.replace(':', '_').replace('/', '_')}-{self._cfg.seed}-{identifier}" - ) - self.save_model( - checkpoint_dir / self.pretrained_model_dir_name, - policy, - wandb_artifact_name=wandb_artifact_name, - ) - self.save_training_state( - checkpoint_dir, train_step, optimizer, scheduler, interaction_step - ) - os.symlink(checkpoint_dir.absolute(), self.last_checkpoint_dir) - - def load_last_training_state( - self, optimizer: Optimizer | dict, scheduler: LRScheduler | None - ) -> int: - """ - Given the last checkpoint in the logging directory, load the optimizer state, scheduler state, and - random state, and return the global training step. - """ - training_state = torch.load( - self.last_checkpoint_dir / self.training_state_file_name - ) - # For the case where the optimizer is a dictionary of optimizers (e.g., sac) - if type(training_state["optimizer"]) is dict: - assert set(training_state["optimizer"].keys()) == set(optimizer.keys()), ( - "Optimizer dictionaries do not have the same keys during resume!" - ) - for k, v in training_state["optimizer"].items(): - optimizer[k].load_state_dict(v) - else: - optimizer.load_state_dict(training_state["optimizer"]) - if scheduler is not None: - scheduler.load_state_dict(training_state["scheduler"]) - elif "scheduler" in training_state: - raise ValueError( - "The checkpoint contains a scheduler state_dict, but no LRScheduler was provided." - ) - # Small hack to get the expected keys: use `get_global_random_state`. - set_global_random_state( - {k: training_state[k] for k in get_global_random_state()} - ) - return training_state["step"] - - def log_dict( - self, - d, - step: int | None = None, - mode="train", - custom_step_key: str | None = None, - ): - """Log a dictionary of metrics to WandB.""" - assert mode in {"train", "eval"} - # TODO(alexander-soare): Add local text log. - if step is None and custom_step_key is None: - raise ValueError("Either step or custom_step_key must be provided.") - - if self._wandb is not None: - # NOTE: This is not simple. Wandb step is it must always monotonically increase and it - # increases with each wandb.log call, but in the case of asynchronous RL for example, - # multiple time steps is possible for example, the interaction step with the environment, - # the training step, the evaluation step, etc. So we need to define a custom step key - # to log the correct step for each metric. - if custom_step_key is not None: - if self._wandb_custom_step_key is None: - self._wandb_custom_step_key = set() - new_custom_key = f"{mode}/{custom_step_key}" - if new_custom_key not in self._wandb_custom_step_key: - self._wandb_custom_step_key.add(new_custom_key) - self._wandb.define_metric(new_custom_key, hidden=True) - - for k, v in d.items(): - if not isinstance(v, (int, float, str, wandb.Table)): - logging.warning( - f'WandB logging of key "{k}" was ignored as its type is not handled by this wrapper.' - ) - continue - - # Do not log the custom step key itself. - if ( - self._wandb_custom_step_key is not None - and k in self._wandb_custom_step_key - ): - continue - - if custom_step_key is not None: - value_custom_step = d[custom_step_key] - self._wandb.log( - { - f"{mode}/{k}": v, - f"{mode}/{custom_step_key}": value_custom_step, - } - ) - continue - - self._wandb.log(data={f"{mode}/{k}": v}, step=step) - - def log_video(self, video_path: str, step: int, mode: str = "train"): - assert mode in {"train", "eval"} - assert self._wandb is not None - wandb_video = self._wandb.Video(video_path, fps=self._cfg.fps, format="mp4") - self._wandb.log({f"{mode}/video": wandb_video}, step=step) diff --git a/lerobot/configs/default.yaml b/lerobot/configs/default.yaml deleted file mode 100644 index 7750ba3a..00000000 --- a/lerobot/configs/default.yaml +++ /dev/null @@ -1,131 +0,0 @@ -defaults: - - _self_ - - env: pusht - - policy: diffusion - - robot: so100 - -hydra: - run: - # Set `dir` to where you would like to save all of the run outputs. If you run another training session - # with the same value for `dir` its contents will be overwritten unless you set `resume` to true. - dir: outputs/train/${now:%Y-%m-%d}/${now:%H-%M-%S}_${env.name}_${policy.name}_${hydra.job.name} - job: - name: default - -# Set `resume` to true to resume a previous run. In order for this to work, you will need to make sure -# `hydra.run.dir` is the directory of an existing run with at least one checkpoint in it. -# Note that when resuming a run, the default behavior is to use the configuration from the checkpoint, -# regardless of what's provided with the training command at the time of resumption. -resume: false -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: ??? -# You may provide a list of datasets here. `train.py` creates them all and concatenates them. Note: only data -# keys common between the datasets are kept. Each dataset gets and additional transform that inserts the -# "dataset_index" into the returned item. The index mapping is made according to the order in which the -# datsets are provided. -dataset_repo_id: lerobot/pusht -video_backend: pyav - -training: - offline_steps: ??? - - # Number of workers for the offline training dataloader. - num_workers: 4 - - batch_size: ??? - - eval_freq: ??? - log_freq: 200 - save_checkpoint: true - # Checkpoint is saved every `save_freq` training iterations and after the last training step. - save_freq: ??? - - # Online training. Note that the online training loop adopts most of the options above apart from the - # dataloader options. Unless otherwise specified. - # The online training look looks something like: - # - # for i in range(online_steps): - # do_online_rollout_and_update_online_buffer() - # for j in range(online_steps_between_rollouts): - # batch = next(dataloader_with_offline_and_online_data) - # loss = policy(batch) - # loss.backward() - # optimizer.step() - # - online_steps: ??? - # How many episodes to collect at once when we reach the online rollout part of the training loop. - online_rollout_n_episodes: 1 - # The number of environments to use in the gym.vector.VectorEnv. This ends up also being the batch size for - # the policy. Ideally you should set this to by an even divisor or online_rollout_n_episodes. - online_rollout_batch_size: 1 - # How many optimization steps (forward, backward, optimizer step) to do between running rollouts. - online_steps_between_rollouts: null - # The proportion of online samples (vs offline samples) to include in the online training batches. - online_sampling_ratio: 0.5 - # First seed to use for the online rollout environment. Seeds for subsequent rollouts are incremented by 1. - online_env_seed: null - # Sets the maximum number of frames that are stored in the online buffer for online training. The buffer is - # FIFO. - online_buffer_capacity: null - # The minimum number of frames to have in the online buffer before commencing online training. - # If online_buffer_seed_size > online_rollout_n_episodes, the rollout will be run multiple times until the - # seed size condition is satisfied. - online_buffer_seed_size: 0 - # Whether to run the online rollouts asynchronously. This means we can run the online training steps in - # parallel with the rollouts. This might be advised if your GPU has the bandwidth to handle training - # + eval + environment rendering simultaneously. - do_online_rollout_async: false - - image_transforms: - # These transforms are all using standard torchvision.transforms.v2 - # You can find out how these transformations affect images here: - # https://pytorch.org/vision/0.18/auto_examples/transforms/plot_transforms_illustrations.html - # We use a custom RandomSubsetApply container to sample them. - # For each transform, the following parameters are available: - # weight: This represents the multinomial probability (with no replacement) - # used for sampling the transform. If the sum of the weights is not 1, - # they will be normalized. - # min_max: Lower & upper bound respectively used for sampling the transform's parameter - # (following uniform distribution) when it's applied. - # Set this flag to `true` to enable transforms during training - enable: false - # This is the maximum number of transforms (sampled from these below) that will be applied to each frame. - # It's an integer in the interval [1, number of available transforms]. - max_num_transforms: 3 - # By default, transforms are applied in Torchvision's suggested order (shown below). - # Set this to True to apply them in a random order. - random_order: false - brightness: - weight: 1 - min_max: [0.8, 1.2] - contrast: - weight: 1 - min_max: [0.8, 1.2] - saturation: - weight: 1 - min_max: [0.5, 1.5] - hue: - weight: 1 - min_max: [-0.05, 0.05] - sharpness: - weight: 1 - min_max: [0.8, 1.2] - -eval: - n_episodes: 1 - # `batch_size` specifies the number of environments to use in a gym.vector.VectorEnv. - batch_size: 1 - # `use_async_envs` specifies whether to use asynchronous environments (multiprocessing). - use_async_envs: false - -wandb: - enable: false - # Set to true to disable saving an artifact despite save_checkpoint == True - disable_artifact: false - project: lerobot - notes: "" diff --git a/lerobot/configs/env/maniskill_example.yaml b/lerobot/configs/env/maniskill_example.yaml deleted file mode 100644 index 2beaa8a6..00000000 --- a/lerobot/configs/env/maniskill_example.yaml +++ /dev/null @@ -1,30 +0,0 @@ -# @package _global_ - -fps: 400 - -env: - name: maniskill/pushcube - task: PushCube-v1 - image_size: 64 - control_mode: pd_ee_delta_pose - state_dim: 25 - action_dim: 7 - fps: ${fps} - obs: rgb - render_mode: rgb_array - render_size: 64 - device: cuda - - reward_classifier: - pretrained_path: null - config_path: null - - wrapper: - joint_masking_action_space: null - delta_action: null - - video_record: - enabled: false - record_dir: maniskill_videos - trajectory_name: trajectory - fps: ${fps} diff --git a/lerobot/configs/env/so100_real.yaml b/lerobot/configs/env/so100_real.yaml deleted file mode 100644 index 5265a252..00000000 --- a/lerobot/configs/env/so100_real.yaml +++ /dev/null @@ -1,50 +0,0 @@ -# @package _global_ - -fps: 10 - -env: - name: real_world - task: null - state_dim: 15 - action_dim: 3 - fps: ${fps} - device: mps - - wrapper: - crop_params_dict: - observation.images.front: [171, 207, 116, 251] - observation.images.side: [232, 200, 142, 204] - resize_size: [128, 128] - control_time_s: 10 - reset_follower_pos: false - use_relative_joint_positions: true - reset_time_s: 5 - display_cameras: false - delta_action: null #0.3 - joint_masking_action_space: null #[1, 1, 1, 1, 0, 0] # disable wrist and gripper - add_joint_velocity_to_observation: true - add_ee_pose_to_observation: true - - # If null then the teleoperation will be used to reset the robot - # Bounds for pushcube_gamepad_lerobot15 dataset and experiments - # fixed_reset_joint_positions: [-19.86, 103.19, 117.33, 42.7, 13.89, 0.297] - # ee_action_space_params: # If null then ee_action_space is not used - # bounds: - # max: [0.291, 0.147, 0.074] - # min: [0.139, -0.143, 0.03] - - # Bounds for insertcube_gamepad dataset and experiments - fixed_reset_joint_positions: [20.0, 90., 90., 75., -0.7910156, -0.5673759] - ee_action_space_params: - bounds: - max: [0.25295413, 0.07498981, 0.06862044] - min: [0.2010096, -0.12, 0.0433196] - - use_gamepad: true - x_step_size: 0.03 - y_step_size: 0.03 - z_step_size: 0.03 - - reward_classifier: - pretrained_path: null # outputs/classifier/13-02-random-sample-resnet10-frozen/checkpoints/best/pretrained_model - config_path: null # lerobot/configs/policy/hilserl_classifier.yaml diff --git a/lerobot/configs/policy/hilserl_classifier.yaml b/lerobot/configs/policy/hilserl_classifier.yaml deleted file mode 100644 index 9ab181d5..00000000 --- a/lerobot/configs/policy/hilserl_classifier.yaml +++ /dev/null @@ -1,61 +0,0 @@ -# @package _global_ - -defaults: - - _self_ - -hydra: - run: - # Set `dir` to where you would like to save all of the run outputs. If you run another training session - # with the same value for `dir` its contents will be overwritten unless you set `resume` to true. - dir: outputs/train_hilserl_classifier/${now:%Y-%m-%d}/${now:%H-%M-%S}_${env.name}_${hydra.job.name} - job: - name: default - -seed: 13 -dataset_repo_id: aractingi/push_cube_square_light_reward_cropped_resized -# aractingi/push_cube_square_reward_1_cropped_resized -dataset_root: data/aractingi/push_cube_square_light_reward_cropped_resized -local_files_only: true -train_split_proportion: 0.8 - -# Required by logger -env: - name: "classifier" - task: "binary_classification" - - -training: - num_epochs: 6 - batch_size: 16 - learning_rate: 1e-4 - num_workers: 4 - grad_clip_norm: 10 - use_amp: true - log_freq: 1 - eval_freq: 1 # How often to run validation (in epochs) - save_freq: 1 # How often to save checkpoints (in epochs) - save_checkpoint: true - image_keys: ["observation.images.front", "observation.images.side"] - label_key: "next.reward" - profile_inference_time: false - profile_inference_time_iters: 20 - -eval: - batch_size: 16 - num_samples_to_log: 30 # Number of validation samples to log in the table - -policy: - name: "hilserl/classifier" - model_name: "helper2424/resnet10" # "facebook/convnext-base-224 - model_type: "cnn" - num_cameras: 2 # Has to be len(training.image_keys) - -wandb: - enable: false - project: "classifier-training" - job_name: "classifier_training_0" - disable_artifact: false - -device: "mps" -resume: false -output_dir: "outputs/classifier/old_trainer_resnet10_frozen" diff --git a/lerobot/configs/policy/sac_maniskill.yaml b/lerobot/configs/policy/sac_maniskill.yaml deleted file mode 100644 index cf20d059..00000000 --- a/lerobot/configs/policy/sac_maniskill.yaml +++ /dev/null @@ -1,118 +0,0 @@ -# @package _global_ - -# Train with: -# -# python lerobot/scripts/train.py \ -# +dataset=lerobot/pusht_keypoints -# env=pusht \ -# env.gym.obs_type=environment_state_agent_pos \ - -seed: 1 -# dataset_repo_id: "AdilZtn/Maniskill-Pushcube-demonstration-medium" -dataset_repo_id: null - -training: - # Offline training dataloader - num_workers: 4 - - batch_size: 512 - grad_clip_norm: 40.0 - lr: 3e-4 - - - storage_device: "cuda" - - eval_freq: 2500 - log_freq: 10 - save_freq: 1000000 - - online_steps: 1000000 - online_rollout_n_episodes: 10 - online_rollout_batch_size: 10 - online_steps_between_rollouts: 1000 - online_sampling_ratio: 1.0 - online_env_seed: 10000 - online_buffer_capacity: 200000 - offline_buffer_capacity: 100000 - online_buffer_seed_size: 0 - online_step_before_learning: 500 - do_online_rollout_async: false - policy_update_freq: 1 - -policy: - name: sac - - pretrained_model_path: - - # Input / output structure. - n_action_repeats: 1 - horizon: 1 - n_action_steps: 1 - - shared_encoder: true - # vision_encoder_name: "helper2424/resnet10" - vision_encoder_name: null - # freeze_vision_encoder: true - freeze_vision_encoder: false - input_shapes: - # # TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env? - observation.state: ["${env.state_dim}"] - observation.image: [3, 64, 64] - output_shapes: - action: [7] - - camera_number: 1 - - # Normalization / Unnormalization - # input_normalization_modes: null - input_normalization_modes: - observation.state: min_max - observation.image: mean_std - # input_normalization_params: null - input_normalization_params: - observation.state: - min: [-1.9361e+00, -7.7640e-01, -7.7094e-01, -2.9709e+00, -8.5656e-01, - 1.0764e+00, -1.2680e+00, 0.0000e+00, 0.0000e+00, -9.3448e+00, - -3.3828e+00, -3.8420e+00, -5.2553e+00, -3.4154e+00, -6.5082e+00, - -6.0500e+00, -8.7193e+00, -8.2337e+00, -3.4650e-01, -4.9441e-01, - 8.3516e-03, -3.1114e-01, -9.9700e-01, -2.3471e-01, -2.7137e-01] - max: [ 0.8644, 1.4306, 1.8520, -0.7578, 0.9508, 3.4901, 1.9381, 0.0400, - 0.0400, 5.0885, 4.7156, 7.9393, 7.9100, 2.9796, 5.7720, 4.7163, - 7.8145, 9.7415, 0.2422, 0.4505, 0.6306, 0.2622, 1.0000, 0.5135, - 0.4001] - - observation.image: - mean: [0.485, 0.456, 0.406] - std: [0.229, 0.224, 0.225] - - output_normalization_modes: - action: min_max - output_normalization_params: - action: - min: [-0.03, -0.03, -0.03, -0.03, -0.03, -0.03, -0.03] - max: [0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03] - output_normalization_shapes: - action: [7] - - # Architecture / modeling. - # Neural networks. - image_encoder_hidden_dim: 32 - # discount: 0.99 - discount: 0.80 - temperature_init: 1.0 - num_critics: 2 #10 - num_subsample_critics: null - critic_lr: 3e-4 - actor_lr: 3e-4 - temperature_lr: 3e-4 - # critic_target_update_weight: 0.005 - critic_target_update_weight: 0.01 - utd_ratio: 2 # 10 - -actor_learner_config: - learner_host: "127.0.0.1" - learner_port: 50051 - policy_parameters_push_frequency: 4 - concurrency: - actor: 'threads' - learner: 'threads' diff --git a/lerobot/configs/policy/sac_pusht_keypoints.yaml b/lerobot/configs/policy/sac_pusht_keypoints.yaml deleted file mode 100644 index 6d8971a2..00000000 --- a/lerobot/configs/policy/sac_pusht_keypoints.yaml +++ /dev/null @@ -1,89 +0,0 @@ -# @package _global_ - -# Train with: -# -# python lerobot/scripts/train.py \ -# env=pusht \ -# +dataset=lerobot/pusht_keypoints - -seed: 1 -dataset_repo_id: lerobot/pusht_keypoints - -training: - offline_steps: 0 - - # Offline training dataloader - num_workers: 4 - - batch_size: 128 - grad_clip_norm: 10.0 - lr: 3e-4 - - eval_freq: 50000 - log_freq: 500 - save_freq: 50000 - - online_steps: 1000000 - online_rollout_n_episodes: 10 - online_rollout_batch_size: 10 - online_steps_between_rollouts: 1000 - online_sampling_ratio: 1.0 - online_env_seed: 10000 - online_buffer_capacity: 40000 - online_buffer_seed_size: 0 - do_online_rollout_async: false - - delta_timestamps: - observation.environment_state: "[i / ${fps} for i in range(${policy.horizon} + 1)]" - observation.state: "[i / ${fps} for i in range(${policy.horizon} + 1)]" - action: "[i / ${fps} for i in range(${policy.horizon})]" - next.reward: "[i / ${fps} for i in range(${policy.horizon})]" - -policy: - name: sac - - pretrained_model_path: - - # Input / output structure. - n_action_repeats: 1 - horizon: 5 - n_action_steps: 5 - - input_shapes: - # TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env? - observation.environment_state: [16] - observation.state: ["${env.state_dim}"] - output_shapes: - action: ["${env.action_dim}"] - - # Normalization / Unnormalization - input_normalization_modes: - observation.environment_state: min_max - observation.state: min_max - output_normalization_modes: - action: min_max - - # Architecture / modeling. - # Neural networks. - # image_encoder_hidden_dim: 32 - discount: 0.99 - temperature_init: 1.0 - num_critics: 2 - num_subsample_critics: None - critic_lr: 3e-4 - actor_lr: 3e-4 - temperature_lr: 3e-4 - critic_target_update_weight: 0.005 - utd_ratio: 2 - - - # # Loss coefficients. - # reward_coeff: 0.5 - # expectile_weight: 0.9 - # value_coeff: 0.1 - # consistency_coeff: 20.0 - # advantage_scaling: 3.0 - # pi_coeff: 0.5 - # temporal_decay_coeff: 0.5 - # # Target model. - # target_model_momentum: 0.995 diff --git a/lerobot/configs/policy/sac_real.yaml b/lerobot/configs/policy/sac_real.yaml deleted file mode 100644 index 039fe0f0..00000000 --- a/lerobot/configs/policy/sac_real.yaml +++ /dev/null @@ -1,120 +0,0 @@ -# @package _global_ - -# Train with: -# -# python lerobot/scripts/train.py \ -# +dataset=lerobot/pusht_keypoints -# env=pusht \ -# env.gym.obs_type=environment_state_agent_pos \ - -seed: 1 -dataset_repo_id: aractingi/insertcube_simple - -training: - # Offline training dataloader - num_workers: 4 - - # batch_size: 256 - batch_size: 512 - grad_clip_norm: 10.0 - lr: 3e-4 - - eval_freq: 2500 - log_freq: 1 - save_freq: 2000000 - - online_steps: 1000000 - online_rollout_n_episodes: 10 - online_rollout_batch_size: 10 - online_steps_between_rollouts: 1000 - online_sampling_ratio: 1.0 - online_env_seed: 10000 - online_buffer_capacity: 10000 - online_buffer_seed_size: 0 - online_step_before_learning: 100 #5000 - do_online_rollout_async: false - policy_update_freq: 1 - - # delta_timestamps: - # observation.environment_state: "[i / ${fps} for i in range(${policy.horizon} + 1)]" - # observation.state: "[i / ${fps} for i in range(${policy.horizon} + 1)]" - # action: "[i / ${fps} for i in range(${policy.horizon})]" - # next.reward: "[i / ${fps} for i in range(${policy.horizon})]" - -policy: - name: sac - - pretrained_model_path: - - # Input / output structure. - n_action_repeats: 1 - horizon: 1 - n_action_steps: 1 - - shared_encoder: true - vision_encoder_name: "helper2424/resnet10" - freeze_vision_encoder: true - input_shapes: - # # TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env? - observation.state: ["${env.state_dim}"] - observation.images.front: [3, 128, 128] - observation.images.side: [3, 128, 128] - # observation.image: [3, 128, 128] - output_shapes: - action: ["${env.action_dim}"] - - # Normalization / Unnormalization - input_normalization_modes: - observation.images.front: mean_std - observation.images.side: mean_std - observation.state: min_max - input_normalization_params: - observation.images.front: - mean: [0.485, 0.456, 0.406] - std: [0.229, 0.224, 0.225] - observation.images.side: - mean: [0.485, 0.456, 0.406] - std: [0.229, 0.224, 0.225] - observation.state: - # 6- joint positions, 6- joint velocities, 3- ee position - max: [ 52.822266, 136.14258, 142.03125, 72.1582, 22.675781, -0.5673759, 100., 100., 100., 100., 100., 100., 0.25295413, 0.07498981, 0.06862044] - min: [-2.6367188, 86.572266, 89.82422, 12.392578, -26.015625, -0.5673759, -100., -100., -100., -100., -100., -100., 0.2010096, -0.12, 0.0433196] - - output_normalization_modes: - action: min_max - output_normalization_params: - action: - min: [-0.03, -0.03, -0.01] - max: [0.03, 0.03, 0.03] - - # Architecture / modeling. - # Neural networks. - image_encoder_hidden_dim: 32 - # discount: 0.99 - discount: 0.97 - temperature_init: 1.0 - num_critics: 2 #10 - camera_number: 2 - num_subsample_critics: null - critic_lr: 3e-4 - actor_lr: 3e-4 - temperature_lr: 3e-4 - # critic_target_update_weight: 0.005 - critic_target_update_weight: 0.01 - utd_ratio: 2 # 10 - -actor_learner_config: - learner_host: "127.0.0.1" - learner_port: 50051 - policy_parameters_push_frequency: 15 - - # # Loss coefficients. - # reward_coeff: 0.5 - # expectile_weight: 0.9 - # value_coeff: 0.1 - # consistency_coeff: 20.0 - # advantage_scaling: 3.0 - # pi_coeff: 0.5 - # temporal_decay_coeff: 0.5 - # # Target model. - # target_model_momentum: 0.995 diff --git a/lerobot/configs/robot/koch.yaml b/lerobot/configs/robot/koch.yaml deleted file mode 100644 index 334db830..00000000 --- a/lerobot/configs/robot/koch.yaml +++ /dev/null @@ -1,53 +0,0 @@ -_target_: lerobot.common.robot_devices.robots.manipulator.ManipulatorRobot -robot_type: koch -calibration_dir: .cache/calibration/koch - -# `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes. -# Set this to a positive scalar to have the same value for all motors, or a list that is the same length as -# the number of motors in your follower arms. -max_relative_target: null - -leader_arms: - main: - _target_: lerobot.common.robot_devices.motors.dynamixel.DynamixelMotorsBus - port: /dev/tty.usbmodem58760430441 - motors: - # name: (index, model) - shoulder_pan: [1, "xl330-m077"] - shoulder_lift: [2, "xl330-m077"] - elbow_flex: [3, "xl330-m077"] - wrist_flex: [4, "xl330-m077"] - wrist_roll: [5, "xl330-m077"] - gripper: [6, "xl330-m077"] - -follower_arms: - main: - _target_: lerobot.common.robot_devices.motors.dynamixel.DynamixelMotorsBus - port: /dev/tty.usbmodem585A0083391 - motors: - # name: (index, model) - shoulder_pan: [1, "xl430-w250"] - shoulder_lift: [2, "xl430-w250"] - elbow_flex: [3, "xl330-m288"] - wrist_flex: [4, "xl330-m288"] - wrist_roll: [5, "xl330-m288"] - gripper: [6, "xl330-m288"] - -cameras: - laptop: - _target_: lerobot.common.robot_devices.cameras.opencv.OpenCVCamera - camera_index: 0 - fps: 30 - width: 640 - height: 480 - phone: - _target_: lerobot.common.robot_devices.cameras.opencv.OpenCVCamera - camera_index: 1 - fps: 30 - width: 640 - height: 480 - -# ~ Koch specific settings ~ -# Sets the leader arm in torque mode with the gripper motor set to this angle. This makes it possible -# to squeeze the gripper and have it spring back to an open position on its own. -gripper_open_degree: 35.156 diff --git a/lerobot/configs/robot/so100.yaml b/lerobot/configs/robot/so100.yaml deleted file mode 100644 index 59ecfa0b..00000000 --- a/lerobot/configs/robot/so100.yaml +++ /dev/null @@ -1,63 +0,0 @@ -# [SO-100 robot arm](https://github.com/TheRobotStudio/SO-ARM100) - -# Requires installing extras packages -# With pip: `pip install -e ".[feetech]"` -# With poetry: `poetry install --sync --extras "feetech"` - -# See [tutorial](https://github.com/huggingface/lerobot/blob/main/examples/10_use_so100.md) - -_target_: lerobot.common.robot_devices.robots.manipulator.ManipulatorRobot -robot_type: so100 -calibration_dir: .cache/calibration/so100 - -# `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes. -# Set this to a positive scalar to have the same value for all motors, or a list that is the same length as -# the number of motors in your follower arms. -max_relative_target: null -joint_position_relative_bounds: null - # max: [100, 100, 100, 100, 100, 100] - # min: [-100, -100, -100, -100, -100, -100] - # max: [ 7.2158203e+01, 1.5398438e+02, 1.6075195e+02, 9.3251953e+01, 0., -1.4184397e-01] - # min: [-77.08008, 56.25, 60.55664, 19.511719, 0., -0.63829786] - # max: [ 35.06836 , 103.18359 , 127.61719 , 75.58594 , 0., 0.] - # min: [ -8.876953 , 63.808594 , 90.49805 , 49.48242 , 0., 0.] - -leader_arms: - main: - _target_: lerobot.common.robot_devices.motors.feetech.FeetechMotorsBus - port: /dev/tty.usbmodem58760433331 - motors: - # name: (index, model) - shoulder_pan: [1, "sts3215"] - shoulder_lift: [2, "sts3215"] - elbow_flex: [3, "sts3215"] - wrist_flex: [4, "sts3215"] - wrist_roll: [5, "sts3215"] - gripper: [6, "sts3215"] - -follower_arms: - main: - _target_: lerobot.common.robot_devices.motors.feetech.FeetechMotorsBus - port: /dev/tty.usbmodem58760431631 - motors: - # name: (index, model) - shoulder_pan: [1, "sts3215"] - shoulder_lift: [2, "sts3215"] - elbow_flex: [3, "sts3215"] - wrist_flex: [4, "sts3215"] - 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