113 lines
3.0 KiB
Python
113 lines
3.0 KiB
Python
import pytest
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from tensordict import TensorDict
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import torch
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from torchrl.envs.utils import check_env_specs, step_mdp
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from lerobot.common.datasets.factory import make_offline_buffer
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from lerobot.common.envs.factory import make_env
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from lerobot.common.envs.pusht.env import PushtEnv
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from lerobot.common.envs.simxarm.env import SimxarmEnv
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from lerobot.common.utils import init_hydra_config
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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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"task,from_pixels,pixels_only",
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[
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("lift", False, False),
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("lift", True, False),
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("lift", True, True),
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# TODO(aliberts): Add simxarm other tasks
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# ("reach", False, False),
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# ("reach", True, False),
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# ("push", False, False),
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# ("push", True, False),
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# ("peg_in_box", False, False),
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# ("peg_in_box", True, False),
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],
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)
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def test_simxarm(task, from_pixels, pixels_only):
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env = SimxarmEnv(
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task,
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from_pixels=from_pixels,
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pixels_only=pixels_only,
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image_size=84 if from_pixels else None,
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)
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# print_spec_rollout(env)
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check_env_specs(env)
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@pytest.mark.parametrize(
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"from_pixels,pixels_only",
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[
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(True, False),
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],
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)
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def test_pusht(from_pixels, pixels_only):
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env = PushtEnv(
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from_pixels=from_pixels,
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pixels_only=pixels_only,
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image_size=96 if from_pixels else None,
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)
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# print_spec_rollout(env)
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check_env_specs(env)
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@pytest.mark.parametrize(
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"env_name",
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[
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"simxarm",
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"pusht",
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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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offline_buffer = make_offline_buffer(cfg)
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env = make_env(cfg)
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for key in offline_buffer.image_keys:
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assert env.reset().get(key).dtype == torch.uint8
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check_env_specs(env)
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env = make_env(cfg, transform=offline_buffer.transform)
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for key in offline_buffer.image_keys:
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img = env.reset().get(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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check_env_specs(env)
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