env: gym.vector.VectorEnv,

policy: Policy,

seeds: list[int] | None = None,

return_observations: bool = False,

render_callback: Callable[[gym.vector.VectorEnv], None] | None = None,

enable_progbar: bool = False,

) -> dict:

"""Run a batched policy rollout once through a batch of environments.

Note that all environments in the batch are run until the last environment is done. This means some

data will probably need to be discarded (for environments that aren't the first one to be done).

The return dictionary contains:

(optional) "observation": A a dictionary of (batch, sequence + 1, *) tensors mapped to observation

keys. NOTE the that this has an extra sequence element relative to the other keys in the

dictionary. This is because an extra observation is included for after the environment is

terminated or truncated.

"action": A (batch, sequence, action_dim) tensor of actions applied based on the observations (not

including the last observations).

"reward": A (batch, sequence) tensor of rewards received for applying the actions.

"success": A (batch, sequence) tensor of success conditions (the only time this can be True is upon

environment termination/truncation).

"done": A (batch, sequence) tensor of **cumulative** done conditions. For any given batch element,

the first True is followed by True's all the way till the end. This can be used for masking

extraneous elements from the sequences above.

Args:

env: The batch of environments.

policy: The policy. Must be a PyTorch nn module.

seeds: The environments are seeded once at the start of the rollout. If provided, this argument

specifies the seeds for each of the environments.

return_observations: Whether to include all observations in the returned rollout data. Observations

are returned optionally because they typically take more memory to cache. Defaults to False.

render_callback: Optional rendering callback to be used after the environments are reset, and after

every step.

enable_progbar: Enable a progress bar over rollout steps.

Returns:

The dictionary described above.

"""

assert isinstance(policy, nn.Module), "Policy must be a PyTorch nn module."

device = get_device_from_parameters(policy)

# Reset the policy and environments.

policy.reset()

observation, info = env.reset(seed=seeds)

if render_callback is not None:

render_callback(env)

all_observations = []

all_actions = []

all_rewards = []

all_successes = []

all_dones = []

step = 0

# Keep track of which environments are done.

done = np.array([False] * env.num_envs)

max_steps = env.call("_max_episode_steps")[0]

progbar = trange(

max_steps,

desc=f"Running rollout with at most {max_steps} steps",

disable=not enable_progbar,

leave=False,

)

while not np.all(done):

# Numpy array to tensor and changing dictionary keys to LeRobot policy format.

observation = preprocess_observation(observation)

if return_observations:

all_observations.append(deepcopy(observation))

observation = {key: observation[key].to(device, non_blocking=True) for key in observation}

with torch.inference_mode():

action = policy.select_action(observation)

# Convert to CPU / numpy.

action = action.to("cpu").numpy()

assert action.ndim == 2, "Action dimensions should be (batch, action_dim)"

# Apply the next action.

observation, reward, terminated, truncated, info = env.step(action)

if render_callback is not None:

render_callback(env)

# VectorEnv stores is_success in `info["final_info"][env_index]["is_success"]`. "final_info" isn't

# available of none of the envs finished.

if "final_info" in info:

successes = [info["is_success"] if info is not None else False for info in info["final_info"]]

else:

successes = [False] * env.num_envs

# Keep track of which environments are done so far.

done = terminated | truncated | done

all_actions.append(torch.from_numpy(action))

all_rewards.append(torch.from_numpy(reward))

all_dones.append(torch.from_numpy(done))

all_successes.append(torch.tensor(successes))

step += 1

running_success_rate = (

einops.reduce(torch.stack(all_successes, dim=1), "b n -> b", "any").numpy().mean()

)

progbar.set_postfix({"running_success_rate": f"{running_success_rate.item() * 100:.1f}%"})

progbar.update()

# Track the final observation.

if return_observations:

observation = preprocess_observation(observation)

all_observations.append(deepcopy(observation))

# Stack the sequence along the first dimension so that we have (batch, sequence, *) tensors.

ret = {

"action": torch.stack(all_actions, dim=1),

"reward": torch.stack(all_rewards, dim=1),

"success": torch.stack(all_successes, dim=1),

"done": torch.stack(all_dones, dim=1),

}

if return_observations:

stacked_observations = {}

for key in all_observations[0]:

stacked_observations[key] = torch.stack([obs[key] for obs in all_observations], dim=1)

ret["observation"] = stacked_observations

env: gym.vector.VectorEnv,

policy: torch.nn.Module,

n_episodes: int,

max_episodes_rendered: int = 0,

videos_dir: Path | None = None,

return_episode_data: bool = False,

start_seed: int | None = None,

enable_progbar: bool = False,

enable_inner_progbar: bool = False,

) -> dict:

"""

Args:

env: The batch of environments.

policy: The policy.

n_episodes: The number of episodes to evaluate.

max_episodes_rendered: Maximum number of episodes to render into videos.

videos_dir: Where to save rendered videos.

return_episode_data: Whether to return episode data for online training. Incorporates the data into

the "episodes" key of the returned dictionary.

start_seed: The first seed to use for the first individual rollout. For all subsequent rollouts the

seed is incremented by 1. If not provided, the environments are not manually seeded.

enable_progbar: Enable progress bar over batches.

enable_inner_progbar: Enable progress bar over steps in each batch.

Returns:

Dictionary with metrics and data regarding the rollouts.

"""

if max_episodes_rendered > 0 and not videos_dir:

raise ValueError("If max_episodes_rendered > 0, videos_dir must be provided.")

assert isinstance(policy, Policy)

start = time.time()

policy.eval()

# Determine how many batched rollouts we need to get n_episodes. Note that if n_episodes is not evenly

# divisible by env.num_envs we end up discarding some data in the last batch.

n_batches = n_episodes // env.num_envs + int((n_episodes % env.num_envs) != 0)

# Keep track of some metrics.

sum_rewards = []

max_rewards = []

all_successes = []

all_seeds = []

threads = [] # for video saving threads

n_episodes_rendered = 0 # for saving the correct number of videos

# Callback for visualization.

def render_frame(env: gym.vector.VectorEnv):

# noqa: B023

if n_episodes_rendered >= max_episodes_rendered:

return

n_to_render_now = min(max_episodes_rendered - n_episodes_rendered, env.num_envs)

if isinstance(env, gym.vector.SyncVectorEnv):

ep_frames.append(np.stack([env.envs[i].render() for i in range(n_to_render_now)])) # noqa: B023

elif isinstance(env, gym.vector.AsyncVectorEnv):

# Here we must render all frames and discard any we don't need.

ep_frames.append(np.stack(env.call("render")[:n_to_render_now]))

if max_episodes_rendered > 0:

video_paths: list[str] = []

if return_episode_data:

episode_data: dict | None = None

progbar = trange(n_batches, desc="Stepping through eval batches", disable=not enable_progbar)

for batch_ix in progbar:

# Cache frames for rendering videos. Each item will be (b, h, w, c), and the list indexes the rollout

# step.

if max_episodes_rendered > 0:

ep_frames: list[np.ndarray] = []

if start_seed is None:

seeds = None

else:

seeds = range(

start_seed + (batch_ix * env.num_envs), start_seed + ((batch_ix + 1) * env.num_envs)

)

rollout_data = rollout(

env,

policy,

seeds=list(seeds) if seeds else None,

return_observations=return_episode_data,

render_callback=render_frame if max_episodes_rendered > 0 else None,

enable_progbar=enable_inner_progbar,

)

# Figure out where in each rollout sequence the first done condition was encountered (results after

# this won't be included).

n_steps = rollout_data["done"].shape[1]

# Note: this relies on a property of argmax: that it returns the first occurrence as a tiebreaker.

done_indices = torch.argmax(rollout_data["done"].to(int), dim=1)

# Make a mask with shape (batch, n_steps) to mask out rollout data after the first done

# (batch-element-wise). Note the `done_indices + 1` to make sure to keep the data from the done step.

mask = (torch.arange(n_steps) <= einops.repeat(done_indices + 1, "b -> b s", s=n_steps)).int()

# Extend metrics.

batch_sum_rewards = einops.reduce((rollout_data["reward"] * mask), "b n -> b", "sum")

sum_rewards.extend(batch_sum_rewards.tolist())

batch_max_rewards = einops.reduce((rollout_data["reward"] * mask), "b n -> b", "max")

max_rewards.extend(batch_max_rewards.tolist())

batch_successes = einops.reduce((rollout_data["success"] * mask), "b n -> b", "any")

all_successes.extend(batch_successes.tolist())

if seeds:

all_seeds.extend(seeds)

else:

all_seeds.append(None)

# FIXME: episode_data is either None or it doesn't exist

if return_episode_data:

this_episode_data = _compile_episode_data(

rollout_data,

done_indices,

start_episode_index=batch_ix * env.num_envs,

start_data_index=(

0 if episode_data is None else (episode_data["episode_data_index"]["to"][-1].item())

),

fps=env.unwrapped.metadata["render_fps"],

)

if episode_data is None:

episode_data = this_episode_data

else:

# Some sanity checks to make sure we are not correctly compiling the data.

assert (

episode_data["hf_dataset"]["episode_index"][-1] + 1

== this_episode_data["hf_dataset"]["episode_index"][0]

)

assert (

episode_data["hf_dataset"]["index"][-1] + 1 == this_episode_data["hf_dataset"]["index"][0]

)

assert torch.equal(

episode_data["episode_data_index"]["to"][-1],

this_episode_data["episode_data_index"]["from"][0],

)

# Concatenate the episode data.

episode_data = {

"hf_dataset": concatenate_datasets(

[episode_data["hf_dataset"], this_episode_data["hf_dataset"]]

),

"episode_data_index": {

k: torch.cat(

[

episode_data["episode_data_index"][k],

this_episode_data["episode_data_index"][k],

]

)

for k in ["from", "to"]

},

}

# Maybe render video for visualization.

if max_episodes_rendered > 0 and len(ep_frames) > 0:

batch_stacked_frames = np.stack(ep_frames, axis=1) # (b, t, *)

for stacked_frames, done_index in zip(

batch_stacked_frames, done_indices.flatten().tolist(), strict=False

):

if n_episodes_rendered >= max_episodes_rendered:

break

videos_dir.mkdir(parents=True, exist_ok=True)

video_path = videos_dir / f"eval_episode_{n_episodes_rendered}.mp4"

video_paths.append(str(video_path))

thread = threading.Thread(

target=write_video,

args=(

str(video_path),

stacked_frames[: done_index + 1], # + 1 to capture the last observation

env.unwrapped.metadata["render_fps"],

),

)

thread.start()

threads.append(thread)

n_episodes_rendered += 1

progbar.set_postfix(

{"running_success_rate": f"{np.mean(all_successes[:n_episodes]).item() * 100:.1f}%"}

)

# Wait till all video rendering threads are done.

for thread in threads:

thread.join()

# Compile eval info.

info = {

"per_episode": [

{

"episode_ix": i,

"sum_reward": sum_reward,

"max_reward": max_reward,

"success": success,

"seed": seed,

}

for i, (sum_reward, max_reward, success, seed) in enumerate(

zip(

sum_rewards[:n_episodes],

max_rewards[:n_episodes],

all_successes[:n_episodes],

all_seeds[:n_episodes],

strict=True,

)

)

],

"aggregated": {

"avg_sum_reward": float(np.nanmean(sum_rewards[:n_episodes])),

"avg_max_reward": float(np.nanmean(max_rewards[:n_episodes])),

"pc_success": float(np.nanmean(all_successes[:n_episodes]) * 100),

"eval_s": time.time() - start,

"eval_ep_s": (time.time() - start) / n_episodes,

},

}

if return_episode_data:

info["episodes"] = episode_data

if max_episodes_rendered > 0:

info["video_paths"] = video_paths

rollout_data: dict, done_indices: Tensor, start_episode_index: int, start_data_index: int, fps: float

) -> dict:

"""Convenience function for `eval_policy(return_episode_data=True)`

Compiles all the rollout data into a Hugging Face dataset.

Similar logic is implemented when datasets are pushed to hub (see: `push_to_hub`).

"""

ep_dicts = []

episode_data_index = {"from": [], "to": []}

total_frames = 0

data_index_from = start_data_index

for ep_ix in range(rollout_data["action"].shape[0]):

num_frames = done_indices[ep_ix].item() + 1 # + 1 to include the first done frame

total_frames += num_frames

# TODO(rcadene): We need to add a missing last frame which is the observation

# of a done state. it is critical to have this frame for tdmpc to predict a "done observation/state"

ep_dict = {

"action": rollout_data["action"][ep_ix, :num_frames],

"episode_index": torch.tensor([start_episode_index + ep_ix] * num_frames),

"frame_index": torch.arange(0, num_frames, 1),

"timestamp": torch.arange(0, num_frames, 1) / fps,

"next.done": rollout_data["done"][ep_ix, :num_frames],

"next.reward": rollout_data["reward"][ep_ix, :num_frames].type(torch.float32),

}

for key in rollout_data["observation"]:

ep_dict[key] = rollout_data["observation"][key][ep_ix][:num_frames]

ep_dicts.append(ep_dict)

episode_data_index["from"].append(data_index_from)

episode_data_index["to"].append(data_index_from + num_frames)

data_index_from += num_frames

data_dict = {}

for key in ep_dicts[0]:

if "image" not in key:

data_dict[key] = torch.cat([x[key] for x in ep_dicts])

else:

if key not in data_dict:

data_dict[key] = []

for ep_dict in ep_dicts:

for img in ep_dict[key]:

# sanity check that images are channel first

c, h, w = img.shape

assert c < h and c < w, f"expect channel first images, but instead {img.shape}"

# sanity check that images are float32 in range [0,1]

assert img.dtype == torch.float32, f"expect torch.float32, but instead {img.dtype=}"

assert img.max() <= 1, f"expect pixels lower than 1, but instead {img.max()=}"

assert img.min() >= 0, f"expect pixels greater than 1, but instead {img.min()=}"

# from float32 in range [0,1] to uint8 in range [0,255]

img *= 255

img = img.type(torch.uint8)

# convert to channel last and numpy as expected by PIL

img = PILImage.fromarray(img.permute(1, 2, 0).numpy())

data_dict[key].append(img)

data_dict["index"] = torch.arange(start_data_index, start_data_index + total_frames, 1)

episode_data_index["from"] = torch.tensor(episode_data_index["from"])

episode_data_index["to"] = torch.tensor(episode_data_index["to"])

# TODO(rcadene): clean this

features = {}

for key in rollout_data["observation"]:

if "image" in key:

features[key] = Image()

else:

features[key] = Sequence(length=data_dict[key].shape[1], feature=Value(dtype="float32", id=None))

features.update(

{

"action": Sequence(length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)),

"episode_index": Value(dtype="int64", id=None),

"frame_index": Value(dtype="int64", id=None),

"timestamp": Value(dtype="float32", id=None),

"next.reward": Value(dtype="float32", id=None),

"next.done": Value(dtype="bool", id=None),

#'next.success': Value(dtype='bool', id=None),

"index": Value(dtype="int64", id=None),

}

)

features = Features(features)

hf_dataset = Dataset.from_dict(data_dict, features=features)

hf_dataset.set_transform(hf_transform_to_torch)

return {

"hf_dataset": hf_dataset,

"episode_data_index": episode_data_index,

pretrained_policy_path: Path | None = None,

hydra_cfg_path: str | None = None,

out_dir: str | None = None,

config_overrides: list[str] | None = None,

):

assert (pretrained_policy_path is None) ^ (hydra_cfg_path is None)

if pretrained_policy_path is not None:

hydra_cfg = init_hydra_config(str(pretrained_policy_path / "config.yaml"), config_overrides)

else:

hydra_cfg = init_hydra_config(hydra_cfg_path, config_overrides)

if out_dir is None:

out_dir = f"outputs/eval/{dt.now().strftime('%Y-%m-%d/%H-%M-%S')}_{hydra_cfg.env.name}_{hydra_cfg.policy.name}"

# Check device is available

device = get_safe_torch_device(hydra_cfg.device, log=True)

torch.backends.cudnn.benchmark = True

torch.backends.cuda.matmul.allow_tf32 = True

set_global_seed(hydra_cfg.seed)

log_output_dir(out_dir)

logging.info("Making environment.")

env = make_env(hydra_cfg)

logging.info("Making policy.")

if hydra_cfg_path is None:

policy = make_policy(hydra_cfg=hydra_cfg, pretrained_policy_name_or_path=str(pretrained_policy_path))

else:

# Note: We need the dataset stats to pass to the policy's normalization modules.

policy = make_policy(hydra_cfg=hydra_cfg, dataset_stats=make_dataset(hydra_cfg).stats)

assert isinstance(policy, nn.Module)

policy.eval()

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,

videos_dir=Path(out_dir) / "videos",

start_seed=hydra_cfg.seed,

enable_progbar=True,

enable_inner_progbar=True,

)

print(info["aggregated"])

# Save info

with open(Path(out_dir) / "eval_info.json", "w") as f:

json.dump(info, f, indent=2)

env.close()