testing syncenv
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@ -17,7 +17,7 @@ import warnings
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from typing import Any
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import einops
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import gym
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import gymnasium as gym
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import numpy as np
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import torch
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from torch import Tensor
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@ -66,7 +66,7 @@ from torch import Tensor, nn
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from tqdm import trange
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from lerobot.common.envs.factory import make_env
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from lerobot.common.envs.utils import check_env_attributes_and_types, preprocess_observation
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from lerobot.common.envs.utils import check_env_attributes_and_types, preprocess_observation, add_envs_task
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from lerobot.common.policies.factory import make_policy
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from lerobot.common.policies.pretrained import PreTrainedPolicy
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from lerobot.common.policies.utils import get_device_from_parameters
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@ -157,7 +157,7 @@ def rollout(
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}
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# Infer "task" from attributes of environments. Works with AsyncVectorEnv.
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observation = addd_envs_task(env, observation)
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observation = add_envs_task(env, observation)
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with torch.inference_mode():
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action = policy.select_action(observation)
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@ -133,7 +133,7 @@ def train(cfg: TrainPipelineConfig):
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eval_env = None
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if cfg.eval_freq > 0 and cfg.env is not None:
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logging.info("Creating env")
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eval_env = make_env(cfg.env, n_envs=cfg.eval.batch_size)
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eval_env = make_env(cfg.env, n_envs=cfg.eval.batch_size, use_async_envs=cfg.eval.use_async_envs)
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logging.info("Creating policy")
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policy = make_policy(
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