Wandb works, One output dir
This commit is contained in:
Cadene
parent
ece89730e6
commit
e3643d6146
25
README.md
25
README.md
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@ -15,6 +15,31 @@ conda activate lerobot
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python setup.py develop
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```
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## Usage
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### Train
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```
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python lerobot/scripts/train.py \
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--config-name=pusht hydra.job.name=pusht
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```
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### Visualize offline buffer
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```
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python lerobot/scripts/visualize_dataset.py \
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--config-name=pusht hydra.run.dir=tmp/$(date +"%Y_%m_%d")
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```
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### Visualize online buffer / Eval
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```
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python lerobot/scripts/eval.py \
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--config-name=pusht hydra.run.dir=tmp/$(date +"%Y_%m_%d")
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```
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## TODO
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- [x] priority update doesnt match FOWM or original paper
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@ -11,6 +11,7 @@ def make_env(cfg):
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"from_pixels": cfg.from_pixels,
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"pixels_only": cfg.pixels_only,
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"image_size": cfg.image_size,
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"max_episode_length": cfg.episode_length,
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}
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if cfg.env == "simxarm":
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@ -29,7 +29,7 @@ class PushtEnv(EnvBase):
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image_size=None,
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seed=1337,
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device="cpu",
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max_episode_length=25, # TODO: verify
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max_episode_length=300,
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):
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super().__init__(device=device, batch_size=[])
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self.frame_skip = frame_skip
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@ -53,13 +53,11 @@ class PushtEnv(EnvBase):
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if not from_pixels:
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raise NotImplementedError("Use PushTEnv, instead of PushTImageEnv")
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from diffusion_policy.env.pusht.pusht_image_env import PushTImageEnv
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from gym.wrappers import TimeLimit
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self._env = PushTImageEnv(render_size=self.image_size)
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self._env = TimeLimit(self._env, self.max_episode_length)
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self._make_spec()
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self.set_seed(seed)
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self._current_seed = self.set_seed(seed)
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def render(self, mode="rgb_array", width=384, height=384):
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if width != height:
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@ -90,7 +88,11 @@ class PushtEnv(EnvBase):
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def _reset(self, tensordict: Optional[TensorDict] = None):
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td = tensordict
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if td is None or td.is_empty():
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# we need to handle seed iteration, since self._env.reset() rely an internal _seed.
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self._current_seed += 1
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self.set_seed(self._current_seed)
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raw_obs = self._env.reset()
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assert self._current_seed == self._env._seed
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td = TensorDict(
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{
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@ -49,7 +49,6 @@ class SimxarmEnv(EnvBase):
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raise ImportError("Cannot import gym.")
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import gym
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from gym.wrappers import TimeLimit
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from simxarm import TASKS
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if self.task not in TASKS:
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@ -58,7 +57,6 @@ class SimxarmEnv(EnvBase):
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)
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self._env = TASKS[self.task]["env"]()
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self._env = TimeLimit(self._env, TASKS[self.task]["episode_length"])
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MAX_NUM_ACTIONS = 4
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num_actions = len(TASKS[self.task]["action_space"])
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@ -11,8 +11,9 @@ from termcolor import colored
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CONSOLE_FORMAT = [
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("episode", "E", "int"),
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("env_step", "S", "int"),
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("avg_reward", "R", "float"),
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("pc_success", "R", "float"),
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("avg_sum_reward", "RS", "float"),
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("avg_max_reward", "RM", "float"),
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("pc_success", "S", "float"),
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("total_time", "T", "time"),
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]
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AGENT_METRICS = [
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@ -69,7 +70,11 @@ def print_run(cfg, reward=None):
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def cfg_to_group(cfg, return_list=False):
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"""Return a wandb-safe group name for logging. Optionally returns group name as list."""
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lst = [cfg.task, cfg.modality, re.sub("[^0-9a-zA-Z]+", "-", cfg.exp_name)]
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# lst = [cfg.task, cfg.modality, re.sub("[^0-9a-zA-Z]+", "-", cfg.exp_name)]
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lst = [
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f"env:{cfg.env}",
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f"seed:{cfg.seed}",
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]
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return lst if return_list else "-".join(lst)
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@ -120,8 +125,9 @@ class VideoRecorder:
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class Logger(object):
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"""Primary logger object. Logs either locally or using wandb."""
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def __init__(self, log_dir, cfg):
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def __init__(self, log_dir, job_name, cfg):
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self._log_dir = make_dir(Path(log_dir))
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self._job_name = job_name
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self._model_dir = make_dir(self._log_dir / "models")
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self._buffer_dir = make_dir(self._log_dir / "buffers")
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self._save_model = cfg.save_model
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@ -131,9 +137,8 @@ class Logger(object):
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self._cfg = cfg
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self._eval = []
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print_run(cfg)
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project, entity = cfg.get("wandb_project", "none"), cfg.get(
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"wandb_entity", "none"
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)
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project = cfg.get("wandb_project", "none")
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entity = cfg.get("wandb_entity", "none")
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run_offline = (
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not cfg.get("use_wandb", False) or project == "none" or entity == "none"
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)
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@ -141,35 +146,39 @@ class Logger(object):
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print(colored("Logs will be saved locally.", "yellow", attrs=["bold"]))
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self._wandb = None
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else:
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try:
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os.environ["WANDB_SILENT"] = "true"
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import wandb
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# try:
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os.environ["WANDB_SILENT"] = "true"
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import wandb
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wandb.init(
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project=project,
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entity=entity,
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name=str(cfg.seed),
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notes=cfg.notes,
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group=self._group,
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tags=cfg_to_group(cfg, return_list=True) + [f"seed:{cfg.seed}"],
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dir=self._log_dir,
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config=OmegaConf.to_container(cfg, resolve=True),
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)
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print(
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colored("Logs will be synced with wandb.", "blue", attrs=["bold"])
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)
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self._wandb = wandb
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except:
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print(
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colored(
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"Warning: failed to init wandb. Make sure `wandb_entity` is set to your username in `config.yaml`. Logs will be saved locally.",
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"yellow",
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attrs=["bold"],
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)
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)
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self._wandb = None
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wandb.init(
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project=project,
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entity=entity,
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name=job_name,
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notes=cfg.notes,
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# group=self._group,
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tags=cfg_to_group(cfg, return_list=True),
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dir=self._log_dir,
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config=OmegaConf.to_container(cfg, resolve=True),
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# TODO(rcadene): try set to True
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save_code=False,
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# TODO(rcadene): split train and eval, and run async eval with job_type="eval"
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job_type="train_eval",
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# TODO(rcadene): add resume option
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resume=None,
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)
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print(colored("Logs will be synced with wandb.", "blue", attrs=["bold"]))
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self._wandb = wandb
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# except:
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# print(
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# colored(
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# "Warning: failed to init wandb. Make sure `wandb_entity` is set to your username in `config.yaml`. Logs will be saved locally.",
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# "yellow",
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# attrs=["bold"],
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# )
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# )
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# self._wandb = None
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self._video = (
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VideoRecorder(log_dir, self._wandb)
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VideoRecorder(self._log_dir, self._wandb)
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if self._wandb and cfg.save_video
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else None
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)
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@ -235,7 +244,7 @@ class Logger(object):
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self._wandb.log({category + "/" + k: v}, step=d["env_step"])
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if category == "eval":
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# keys = ['env_step', 'avg_reward']
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keys = ["env_step", "avg_reward", "pc_success"]
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keys = ["env_step", "avg_sum_reward", "avg_max_reward", "pc_success"]
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self._eval.append(np.array([d[key] for key in keys]))
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pd.DataFrame(np.array(self._eval)).to_csv(
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self._log_dir / "eval.log", header=keys, index=None
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@ -96,7 +96,7 @@ class TDMPC(nn.Module):
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self.model_target.eval()
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self.batch_size = cfg.batch_size
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self.step = 0
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self.register_buffer("step", torch.zeros(1))
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def state_dict(self):
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"""Retrieve state dict of TOLD model, including slow-moving target network."""
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@ -122,7 +122,7 @@ class TDMPC(nn.Module):
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"rgb": observation["image"],
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"state": observation["state"],
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}
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return self.act(obs, t0=t0, step=self.step)
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return self.act(obs, t0=t0, step=self.step.item())
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@torch.no_grad()
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def act(self, obs, t0=False, step=None):
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@ -513,5 +513,5 @@ class TDMPC(nn.Module):
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metrics.update(value_info)
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metrics.update(pi_update_info)
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self.step = step
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self.step[0] = step
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return metrics
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@ -1,7 +1,10 @@
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hydra:
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run:
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dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}_${hydra.job.name}
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job:
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name: default
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seed: 1337
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log_dir: logs/2024_01_26_train
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video_dir: tmp/2024_01_26_xarm_lift_medium
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exp_name: default
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device: cuda
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buffer_device: cuda
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eval_freq: 1000
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@ -21,6 +24,7 @@ fps: 15
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reward_scale: 1.0
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# xarm_lift
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episode_length: 25
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modality: 'all'
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@ -97,6 +101,7 @@ mlp_dim: 512
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latent_dim: 50
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# wandb
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use_wandb: false
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wandb_project: FOWM
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use_wandb: true
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wandb_project: lerobot
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wandb_entity: rcadene # insert your own
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notes: ""
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@ -5,12 +5,16 @@ hydra:
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job:
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name: pusht
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video_dir: tmp/2024_02_21_pusht
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# env
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env: pusht
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task: pusht
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image_size: 96
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frame_skip: 1
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state_dim: 2
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action_dim: 2
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fps: 10
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fps: 10
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eval_episodes: 50
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episode_length: 300
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eval_freq: 7500
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save_freq: 75000
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@ -21,19 +21,22 @@ def eval_policy(
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save_video: bool = False,
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video_dir: Path = None,
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fps: int = 15,
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env_step: int = None,
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wandb=None,
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):
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rewards = []
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if wandb is not None:
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assert env_step is not None
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sum_rewards = []
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max_rewards = []
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successes = []
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for i in range(num_episodes):
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ep_frames = []
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def rendering_callback(env, td=None):
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nonlocal ep_frames
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frame = env.render()
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ep_frames.append(frame)
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ep_frames.append(env.render())
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tensordict = env.reset()
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if save_video:
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if save_video or wandb:
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# render first frame before rollout
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rendering_callback(env)
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@ -41,35 +44,54 @@ def eval_policy(
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rollout = env.rollout(
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max_steps=max_steps,
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policy=policy,
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callback=rendering_callback if save_video else None,
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callback=rendering_callback if save_video or wandb else None,
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auto_reset=False,
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tensordict=tensordict,
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auto_cast_to_device=True,
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)
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# print(", ".join([f"{x:.3f}" for x in rollout["next", "reward"][:,0].tolist()]))
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ep_reward = rollout["next", "reward"].sum()
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ep_sum_reward = rollout["next", "reward"].sum()
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ep_max_reward = rollout["next", "reward"].max()
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ep_success = rollout["next", "success"].any()
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rewards.append(ep_reward.item())
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sum_rewards.append(ep_sum_reward.item())
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max_rewards.append(ep_max_reward.item())
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successes.append(ep_success.item())
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if save_video:
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video_dir.mkdir(parents=True, exist_ok=True)
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# TODO(rcadene): make fps configurable
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video_path = video_dir / f"eval_episode_{i}.mp4"
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imageio.mimsave(video_path, np.stack(ep_frames), fps=fps)
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if save_video or wandb:
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stacked_frames = np.stack(ep_frames)
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if save_video:
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video_dir.mkdir(parents=True, exist_ok=True)
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video_path = video_dir / f"eval_episode_{i}.mp4"
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imageio.mimsave(video_path, stacked_frames, fps=fps)
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first_episode = i == 0
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if wandb and first_episode:
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eval_video = wandb.Video(
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stacked_frames.transpose(0, 3, 1, 2), fps=fps, format="mp4"
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)
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wandb.log({"eval_video": eval_video}, step=env_step)
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metrics = {
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"avg_reward": np.nanmean(rewards),
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"avg_sum_reward": np.nanmean(sum_rewards),
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"avg_max_reward": np.nanmean(max_rewards),
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"pc_success": np.nanmean(successes) * 100,
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}
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return metrics
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@hydra.main(version_base=None, config_name="default", config_path="../configs")
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def eval(cfg: dict):
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def eval_cli(cfg: dict):
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eval(cfg, out_dir=hydra.core.hydra_config.HydraConfig.get().runtime.output_dir)
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def eval(cfg: dict, out_dir=None):
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if out_dir is None:
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raise NotImplementedError()
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assert torch.cuda.is_available()
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set_seed(cfg.seed)
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print(colored("Log dir:", "yellow", attrs=["bold"]), cfg.log_dir)
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print(colored("Log dir:", "yellow", attrs=["bold"]), out_dir)
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env = make_env(cfg)
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@ -95,13 +117,14 @@ def eval(cfg: dict):
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metrics = eval_policy(
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env,
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policy=policy,
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num_episodes=20,
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save_video=True,
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video_dir=Path(cfg.video_dir),
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video_dir=Path(out_dir) / "eval",
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fps=cfg.fps,
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max_steps=cfg.episode_length,
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num_episodes=cfg.eval_episodes,
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)
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print(metrics)
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if __name__ == "__main__":
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eval()
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eval_cli()
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@ -20,24 +20,47 @@ from lerobot.scripts.eval import eval_policy
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@hydra.main(version_base=None, config_name="default", config_path="../configs")
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def train(cfg: dict):
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def train_cli(cfg: dict):
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train(
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cfg,
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out_dir=hydra.core.hydra_config.HydraConfig.get().run.dir,
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job_name=hydra.core.hydra_config.HydraConfig.get().job.name,
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)
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def train_notebook(
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out_dir=None, job_name=None, config_name="default", config_path="../configs"
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):
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from hydra import compose, initialize
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hydra.core.global_hydra.GlobalHydra.instance().clear()
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initialize(config_path=config_path)
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cfg = compose(config_name=config_name)
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train(cfg, out_dir=out_dir, job_name=job_name)
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def train(cfg: dict, out_dir=None, job_name=None):
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if out_dir is None:
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raise NotImplementedError()
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if job_name is None:
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raise NotImplementedError()
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assert torch.cuda.is_available()
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set_seed(cfg.seed)
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print(colored("Work dir:", "yellow", attrs=["bold"]), cfg.log_dir)
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print(colored("Work dir:", "yellow", attrs=["bold"]), out_dir)
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env = make_env(cfg)
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policy = TDMPC(cfg)
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if cfg.pretrained_model_path:
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ckpt_path = (
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"/home/rcadene/code/fowm/logs/xarm_lift/all/default/2/models/offline.pt"
|
||||
)
|
||||
if "offline" in cfg.pretrained_model_path:
|
||||
policy.step = 25000
|
||||
elif "final" in cfg.pretrained_model_path:
|
||||
policy.step = 100000
|
||||
else:
|
||||
raise NotImplementedError()
|
||||
policy.load(ckpt_path)
|
||||
# TODO(rcadene): hack for old pretrained models from fowm
|
||||
if "fowm" in cfg.pretrained_model_path:
|
||||
if "offline" in cfg.pretrained_model_path:
|
||||
policy.step = 25000
|
||||
elif "final" in cfg.pretrained_model_path:
|
||||
policy.step = 100000
|
||||
else:
|
||||
raise NotImplementedError()
|
||||
policy.load(cfg.pretrained_model_path)
|
||||
|
||||
td_policy = TensorDictModule(
|
||||
policy,
|
||||
|
|
@ -65,7 +88,7 @@ def train(cfg: dict):
|
|||
sampler=online_sampler,
|
||||
)
|
||||
|
||||
L = Logger(cfg.log_dir, cfg)
|
||||
L = Logger(out_dir, job_name, cfg)
|
||||
|
||||
online_episode_idx = 0
|
||||
start_time = time.time()
|
||||
|
|
@ -95,12 +118,14 @@ def train(cfg: dict):
|
|||
)
|
||||
online_buffer.extend(rollout)
|
||||
|
||||
ep_reward = rollout["next", "reward"].sum()
|
||||
ep_sum_reward = rollout["next", "reward"].sum()
|
||||
ep_max_reward = rollout["next", "reward"].max()
|
||||
ep_success = rollout["next", "success"].any()
|
||||
|
||||
online_episode_idx += 1
|
||||
rollout_metrics = {
|
||||
"avg_reward": np.nanmean(ep_reward),
|
||||
"avg_sum_reward": np.nanmean(ep_sum_reward),
|
||||
"avg_max_reward": np.nanmean(ep_max_reward),
|
||||
"pc_success": np.nanmean(ep_success) * 100,
|
||||
}
|
||||
num_updates = len(rollout) * cfg.utd
|
||||
|
|
@ -137,23 +162,23 @@ def train(cfg: dict):
|
|||
env,
|
||||
td_policy,
|
||||
num_episodes=cfg.eval_episodes,
|
||||
# TODO(rcadene): add step, env_step, L.video
|
||||
env_step=env_step,
|
||||
wandb=L._wandb,
|
||||
)
|
||||
|
||||
common_metrics.update(eval_metrics)
|
||||
|
||||
L.log(common_metrics, category="eval")
|
||||
last_log_step = env_step - env_step % cfg.eval_freq
|
||||
|
||||
# Save model periodically
|
||||
# if cfg.save_model and env_step - last_save_step >= cfg.save_freq:
|
||||
# L.save_model(policy, identifier=env_step)
|
||||
# print(f"Model has been checkpointed at step {env_step}")
|
||||
# last_save_step = env_step - env_step % cfg.save_freq
|
||||
if cfg.save_model and env_step - last_save_step >= cfg.save_freq:
|
||||
L.save_model(policy, identifier=env_step)
|
||||
print(f"Model has been checkpointed at step {env_step}")
|
||||
last_save_step = env_step - env_step % cfg.save_freq
|
||||
|
||||
# if cfg.save_model and is_offline and _step >= cfg.offline_steps:
|
||||
# # save the model after offline training
|
||||
# L.save_model(policy, identifier="offline")
|
||||
if cfg.save_model and is_offline and _step >= cfg.offline_steps:
|
||||
# save the model after offline training
|
||||
L.save_model(policy, identifier="offline")
|
||||
|
||||
step = _step
|
||||
|
||||
|
|
@ -177,4 +202,4 @@ def train(cfg: dict):
|
|||
|
||||
|
||||
if __name__ == "__main__":
|
||||
train()
|
||||
train_cli()
|
||||
|
|
|
|||
|
|
@ -15,7 +15,15 @@ from lerobot.common.datasets.factory import make_offline_buffer
|
|||
|
||||
|
||||
@hydra.main(version_base=None, config_name="default", config_path="../configs")
|
||||
def visualize_dataset(cfg: dict):
|
||||
def visualize_dataset_cli(cfg: dict):
|
||||
visualize_dataset(
|
||||
cfg, out_dir=hydra.core.hydra_config.HydraConfig.get().runtime.output_dir
|
||||
)
|
||||
|
||||
|
||||
def visualize_dataset(cfg: dict, out_dir=None):
|
||||
if out_dir is None:
|
||||
raise NotImplementedError()
|
||||
|
||||
sampler = SliceSamplerWithoutReplacement(
|
||||
num_slices=1,
|
||||
|
|
@ -40,10 +48,10 @@ def visualize_dataset(cfg: dict):
|
|||
dim=0,
|
||||
)
|
||||
|
||||
video_dir = Path(cfg.video_dir)
|
||||
video_dir = Path(out_dir) / "visualize_dataset"
|
||||
video_dir.mkdir(parents=True, exist_ok=True)
|
||||
# TODO(rcadene): make fps configurable
|
||||
video_path = video_dir / f"eval_episode_{ep_idx}.mp4"
|
||||
video_path = video_dir / f"episode_{ep_idx}.mp4"
|
||||
|
||||
assert ep_frames.min().item() >= 0
|
||||
assert ep_frames.max().item() > 1, "Not mendatory, but sanity check"
|
||||
|
|
@ -59,4 +67,4 @@ def visualize_dataset(cfg: dict):
|
|||
|
||||
|
||||
if __name__ == "__main__":
|
||||
visualize_dataset()
|
||||
visualize_dataset_cli()
|
||||
|
|
|
|||
Loading…
Reference in New Issue