64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
import importlib
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import pytest
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import torch
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from lerobot.common.datasets.factory import make_dataset
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import gymnasium as gym
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from gymnasium.utils.env_checker import check_env
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from lerobot.common.envs.factory import make_env
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from lerobot.common.utils import init_hydra_config
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from lerobot.common.envs.utils import preprocess_observation
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from .utils import DEVICE, DEFAULT_CONFIG_PATH
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@pytest.mark.parametrize(
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"env_name, task, obs_type",
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[
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# ("AlohaInsertion-v0", "state"),
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("aloha", "AlohaInsertion-v0", "pixels"),
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("aloha", "AlohaInsertion-v0", "pixels_agent_pos"),
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("aloha", "AlohaTransferCube-v0", "pixels"),
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("aloha", "AlohaTransferCube-v0", "pixels_agent_pos"),
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("xarm", "XarmLift-v0", "state"),
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("xarm", "XarmLift-v0", "pixels"),
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("xarm", "XarmLift-v0", "pixels_agent_pos"),
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("pusht", "PushT-v0", "state"),
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("pusht", "PushT-v0", "pixels"),
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("pusht", "PushT-v0", "pixels_agent_pos"),
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],
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)
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def test_env(env_name, task, obs_type):
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package_name = f"gym_{env_name}"
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importlib.import_module(package_name)
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env = gym.make(f"{package_name}/{task}", obs_type=obs_type)
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check_env(env.unwrapped)
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@pytest.mark.parametrize(
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"env_name",
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[
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"pusht",
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"xarm",
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"aloha",
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],
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)
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def test_factory(env_name):
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cfg = init_hydra_config(
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DEFAULT_CONFIG_PATH,
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overrides=[f"env={env_name}", f"device={DEVICE}"],
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)
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dataset = make_dataset(cfg)
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env = make_env(cfg, num_parallel_envs=1)
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obs, _ = env.reset()
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obs = preprocess_observation(obs, transform=dataset.transform)
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for key in dataset.image_keys:
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img = obs[key]
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assert img.dtype == torch.float32
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# TODO(rcadene): we assume for now that image normalization takes place in the model
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assert img.max() <= 1.0
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assert img.min() >= 0.0
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