117 lines
3.1 KiB
Python
117 lines
3.1 KiB
Python
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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# import dmc_aloha # noqa: F401
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from .utils import DEVICE, DEFAULT_CONFIG_PATH
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# def print_spec_rollout(env):
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# print("observation_spec:", env.observation_spec)
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# print("action_spec:", env.action_spec)
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# print("reward_spec:", env.reward_spec)
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# print("done_spec:", env.done_spec)
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# td = env.reset()
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# print("reset tensordict", td)
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# td = env.rand_step(td)
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# print("random step tensordict", td)
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# def simple_rollout(steps=100):
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# # preallocate:
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# data = TensorDict({}, [steps])
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# # reset
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# _data = env.reset()
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# for i in range(steps):
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# _data["action"] = env.action_spec.rand()
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# _data = env.step(_data)
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# data[i] = _data
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# _data = step_mdp(_data, keep_other=True)
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# return data
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# print("data from rollout:", simple_rollout(100))
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@pytest.mark.parametrize(
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"env_task, obs_type",
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[
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# ("AlohaInsertion-v0", "state"),
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("AlohaInsertion-v0", "pixels"),
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("AlohaInsertion-v0", "pixels_agent_pos"),
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("AlohaTransferCube-v0", "pixels"),
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("AlohaTransferCube-v0", "pixels_agent_pos"),
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],
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)
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def test_aloha(env_task, obs_type):
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from lerobot.common.envs import aloha as gym_aloha # noqa: F401
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env = gym.make(f"gym_aloha/{env_task}", obs_type=obs_type)
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check_env(env)
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@pytest.mark.parametrize(
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"env_task, obs_type",
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[
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("XarmLift-v0", "state"),
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("XarmLift-v0", "pixels"),
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("XarmLift-v0", "pixels_agent_pos"),
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# TODO(aliberts): Add gym_xarm other tasks
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],
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)
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def test_xarm(env_task, obs_type):
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import gym_xarm # noqa: F401
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env = gym.make(f"gym_xarm/{env_task}", obs_type=obs_type)
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check_env(env)
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@pytest.mark.parametrize(
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"env_task, obs_type",
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[
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("PushTPixels-v0", "state"),
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("PushTPixels-v0", "pixels"),
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("PushTPixels-v0", "pixels_agent_pos"),
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],
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)
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def test_pusht(env_task, obs_type):
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import gym_pusht # noqa: F401
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env = gym.make(f"gym_pusht/{env_task}", obs_type=obs_type)
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check_env(env)
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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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"simxarm",
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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)
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obs, info = env.reset()
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obs = {key: obs[key][None, ...] for key in obs}
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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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