1cdfbc8b52
WIP WIP train.py works, loss going down WIP eval.py Fix WIP (eval running, TODO: verify results reproduced) Eval works! (testing reproducibility) WIP pretrained model pusht reproduces same results as torchrl pretrained model pusht reproduces same results as torchrl Remove AbstractPolicy, Move all queues in select_action WIP test_datasets passed (TODO: re-enable NormalizeTransform)
340 lines
12 KiB
Python
340 lines
12 KiB
Python
"""Evaluate a policy on an environment by running rollouts and computing metrics.
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The script may be run in one of two ways:
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1. By providing the path to a config file with the --config argument.
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2. By providing a HuggingFace Hub ID with the --hub-id argument. You may also provide a revision number with the
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--revision argument.
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In either case, it is possible to override config arguments by adding a list of config.key=value arguments.
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Examples:
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You have a specific config file to go with trained model weights, and want to run 10 episodes.
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```
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python lerobot/scripts/eval.py \
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--config PATH/TO/FOLDER/config.yaml \
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policy.pretrained_model_path=PATH/TO/FOLDER/weights.pth \
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eval_episodes=10
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```
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You have a HuggingFace Hub ID, you know which revision you want, and want to run 10 episodes (note that in this case,
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you don't need to specify which weights to use):
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```
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python lerobot/scripts/eval.py --hub-id HUB/ID --revision v1.0 eval_episodes=10
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```
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"""
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import argparse
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import json
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import logging
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import threading
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import time
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from datetime import datetime as dt
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from pathlib import Path
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import einops
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import gymnasium as gym
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import hydra
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import imageio
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import numpy as np
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import torch
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from huggingface_hub import snapshot_download
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from lerobot.common.datasets.factory import make_dataset
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from lerobot.common.envs.factory import make_env
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from lerobot.common.logger import log_output_dir
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from lerobot.common.policies.factory import make_policy
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from lerobot.common.utils import get_safe_torch_device, init_hydra_config, init_logging, set_global_seed
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from lerobot.common.transforms import apply_inverse_transform
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def write_video(video_path, stacked_frames, fps):
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imageio.mimsave(video_path, stacked_frames, fps=fps)
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def preprocess_observation(observation, transform=None):
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# map to expected inputs for the policy
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obs = {
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"observation.image": torch.from_numpy(observation["pixels"]).float(),
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"observation.state": torch.from_numpy(observation["agent_pos"]).float(),
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}
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# convert to (b c h w) torch format
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obs["observation.image"] = einops.rearrange(obs["observation.image"], "b h w c -> b c h w")
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# apply same transforms as in training
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if transform is not None:
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for key in obs:
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obs[key] = torch.stack([transform({key: item})[key] for item in obs[key]])
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return obs
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def postprocess_action(action, transform=None):
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action = action.to("cpu")
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# action is a batch (num_env,action_dim) instead of an item (action_dim),
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# we assume applying inverse transform on a batch works the same
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action = apply_inverse_transform({"action": action}, transform)["action"].numpy()
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assert (
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action.ndim == 2
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), "we assume dimensions are respectively the number of parallel envs, action dimensions"
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return action
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def eval_policy(
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env: gym.vector.VectorEnv,
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policy,
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save_video: bool = False,
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video_dir: Path = None,
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# TODO(rcadene): make it possible to overwrite fps? we should use env.fps
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fps: int = 15,
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return_first_video: bool = False,
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transform: callable = None,
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):
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if policy is not None:
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policy.eval()
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start = time.time()
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sum_rewards = []
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max_rewards = []
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all_successes = []
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seeds = []
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threads = [] # for video saving threads
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episode_counter = 0 # for saving the correct number of videos
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num_episodes = len(env.envs)
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# TODO(alexander-soare): if num_episodes is not evenly divisible by the batch size, this will do more work than
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# needed as I'm currently taking a ceil.
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ep_frames = []
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def maybe_render_frame(env):
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if save_video: # noqa: B023
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if return_first_video:
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visu = env.envs[0].render()
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visu = visu[None, ...] # add batch dim
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else:
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visu = np.stack([env.render() for env in env.envs])
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ep_frames.append(visu) # noqa: B023
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for _ in range(num_episodes):
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seeds.append("TODO")
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if hasattr(policy, "reset"):
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policy.reset()
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else:
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logging.warning(
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f"Policy {policy} doesnt have a `reset` method. This find if the policy doesnt rely on an internal state during rollout."
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)
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# reset the environment
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observation, info = env.reset(seed=cfg.seed)
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maybe_render_frame(env)
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rewards = []
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successes = []
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dones = []
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done = torch.tensor([False for _ in env.envs])
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step = 0
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do_rollout = True
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while do_rollout:
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# apply transform to normalize the observations
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observation = preprocess_observation(observation, transform)
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# send observation to device/gpu
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observation = {key: observation[key].to(cfg.device, non_blocking=True) for key in observation}
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# get the next action for the environment
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with torch.inference_mode():
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action = policy.select_action(observation, step)
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# apply inverse transform to unnormalize the action
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action = postprocess_action(action, transform)
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# apply the next
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observation, reward, terminated, truncated, info = env.step(action)
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maybe_render_frame(env)
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# TODO(rcadene): implement a wrapper over env to return torch tensors in float32 (and cuda?)
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reward = torch.from_numpy(reward)
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terminated = torch.from_numpy(terminated)
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truncated = torch.from_numpy(truncated)
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# environment is considered done (no more steps), when success state is reached (terminated is True),
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# or time limit is reached (truncated is True), or it was previsouly done.
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done = terminated | truncated | done
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if "final_info" in info:
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# VectorEnv stores is_success into `info["final_info"][env_id]["is_success"]` instead of `info["is_success"]`
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success = [
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env_info["is_success"] if env_info is not None else False for env_info in info["final_info"]
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]
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else:
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success = [False for _ in env.envs]
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success = torch.tensor(success)
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rewards.append(reward)
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dones.append(done)
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successes.append(success)
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step += 1
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if done.all():
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do_rollout = False
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break
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rewards = torch.stack(rewards, dim=1)
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successes = torch.stack(successes, dim=1)
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dones = torch.stack(dones, dim=1)
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# Figure out where in each rollout sequence the first done condition was encountered (results after
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# this won't be included).
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# Note: this assumes that the shape of the done key is (batch_size, max_steps).
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# Note: this relies on a property of argmax: that it returns the first occurrence as a tiebreaker.
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done_indices = torch.argmax(dones.to(int), axis=1) # (batch_size, rollout_steps)
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expand_done_indices = done_indices[:, None].expand(-1, step)
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expand_step_indices = torch.arange(step)[None, :].expand(num_episodes, -1)
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mask = (expand_step_indices <= expand_done_indices).int() # (batch_size, rollout_steps)
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batch_sum_reward = einops.reduce((rewards * mask), "b n -> b", "sum")
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batch_max_reward = einops.reduce((rewards * mask), "b n -> b", "max")
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batch_success = einops.reduce((successes * mask), "b n -> b", "any")
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sum_rewards.extend(batch_sum_reward.tolist())
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max_rewards.extend(batch_max_reward.tolist())
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all_successes.extend(batch_success.tolist())
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env.close()
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if save_video or return_first_video:
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batch_stacked_frames = np.stack(ep_frames, 1) # (b, t, *)
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if save_video:
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for stacked_frames, done_index in zip(
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batch_stacked_frames, done_indices.flatten().tolist(), strict=False
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):
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if episode_counter >= num_episodes:
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continue
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video_dir.mkdir(parents=True, exist_ok=True)
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video_path = video_dir / f"eval_episode_{episode_counter}.mp4"
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thread = threading.Thread(
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target=write_video,
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args=(str(video_path), stacked_frames[:done_index], fps),
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)
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thread.start()
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threads.append(thread)
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episode_counter += 1
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if return_first_video:
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first_video = batch_stacked_frames[0].transpose(0, 3, 1, 2)
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for thread in threads:
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thread.join()
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info = {
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"per_episode": [
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{
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"episode_ix": i,
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"sum_reward": sum_reward,
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"max_reward": max_reward,
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"success": success,
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"seed": seed,
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}
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for i, (sum_reward, max_reward, success, seed) in enumerate(
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zip(
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sum_rewards[:num_episodes],
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max_rewards[:num_episodes],
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all_successes[:num_episodes],
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seeds[:num_episodes],
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strict=True,
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)
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)
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],
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"aggregated": {
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"avg_sum_reward": float(np.nanmean(sum_rewards[:num_episodes])),
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"avg_max_reward": float(np.nanmean(max_rewards[:num_episodes])),
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"pc_success": float(np.nanmean(all_successes[:num_episodes]) * 100),
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"eval_s": time.time() - start,
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"eval_ep_s": (time.time() - start) / num_episodes,
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},
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}
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if return_first_video:
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return info, first_video
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return info
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def eval(cfg: dict, out_dir=None, stats_path=None):
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if out_dir is None:
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raise NotImplementedError()
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init_logging()
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# Check device is available
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get_safe_torch_device(cfg.device, log=True)
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torch.backends.cudnn.benchmark = True
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torch.backends.cuda.matmul.allow_tf32 = True
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set_global_seed(cfg.seed)
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log_output_dir(out_dir)
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logging.info("Making transforms.")
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# TODO(alexander-soare): Completely decouple datasets from evaluation.
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dataset = make_dataset(cfg, stats_path=stats_path)
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logging.info("Making environment.")
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env = make_env(cfg, num_parallel_envs=cfg.eval_episodes)
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# when policy is None, rollout a random policy
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policy = make_policy(cfg) if cfg.policy.pretrained_model_path else None
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info = eval_policy(
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env,
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policy=policy,
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save_video=True,
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video_dir=Path(out_dir) / "eval",
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fps=cfg.env.fps,
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# TODO(rcadene): what should we do with the transform?
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transform=dataset.transform,
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)
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print(info["aggregated"])
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# Save info
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with open(Path(out_dir) / "eval_info.json", "w") as f:
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json.dump(info, f, indent=2)
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logging.info("End of eval")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
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)
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group = parser.add_mutually_exclusive_group(required=True)
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group.add_argument("--config", help="Path to a specific yaml config you want to use.")
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group.add_argument("--hub-id", help="HuggingFace Hub ID for a pretrained model.")
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parser.add_argument("--revision", help="Optionally provide the HuggingFace Hub revision ID.")
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parser.add_argument(
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"overrides",
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nargs="*",
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help="Any key=value arguments to override config values (use dots for.nested=overrides)",
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)
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args = parser.parse_args()
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if args.config is not None:
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# Note: For the config_path, Hydra wants a path relative to this script file.
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cfg = init_hydra_config(args.config, args.overrides)
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# TODO(alexander-soare): Save and load stats in trained model directory.
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stats_path = None
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elif args.hub_id is not None:
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folder = Path(snapshot_download(args.hub_id, revision=args.revision))
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cfg = init_hydra_config(
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folder / "config.yaml", [f"policy.pretrained_model_path={folder / 'model.pt'}", *args.overrides]
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)
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stats_path = folder / "stats.pth"
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eval(
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cfg,
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out_dir=f"outputs/eval/{dt.now().strftime('%Y-%m-%d/%H-%M-%S')}_{cfg.env.name}_{cfg.policy.name}",
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stats_path=stats_path,
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)
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