forked from tangger/lerobot
9d002032d1
WIP Aloha env tests pass Rendering works (fps look fast tho? TODO action bounding is too wide [-1,1]) Update README Copy past from act repo Remove download.py add a WIP for Simxarm Remove download.py add a WIP for Simxarm Add act yaml (TODO: try train.py) Training can runs (TODO: eval) Add tasks without end_effector that are compatible with dataset, Eval can run (TODO: training and pretrained model) Add AbstractEnv, Refactor AlohaEnv, Add rendering_hook in env, Minor modifications, (TODO: Refactor Pusht and Simxarm) poetry lock fix bug in compute_stats for action normalization fix more bugs in normalization fix training fix import PushtEnv inheriates AbstractEnv, Improve factory Normalization Add _make_env to EnvAbstract Add call_rendering_hooks to pusht env SimxarmEnv inherites from AbstractEnv (NOT TESTED) Add aloha tests artifacts + update pusht stats fix image normalization: before env was in [0,1] but dataset in [0,255], and now both in [0,255] Small fix on simxarm Add next to obs Add top camera to Aloha env (TODO: make it compatible with set of cameras) Add top camera to Aloha env (TODO: make it compatible with set of cameras)
109 lines
2.8 KiB
Python
109 lines
2.8 KiB
Python
import pytest
|
|
from tensordict import TensorDict
|
|
import torch
|
|
from torchrl.envs.utils import check_env_specs, step_mdp
|
|
from lerobot.common.datasets.factory import make_offline_buffer
|
|
|
|
from lerobot.common.envs.factory import make_env
|
|
from lerobot.common.envs.pusht.env import PushtEnv
|
|
from lerobot.common.envs.simxarm import SimxarmEnv
|
|
|
|
from .utils import init_config
|
|
|
|
|
|
def print_spec_rollout(env):
|
|
print("observation_spec:", env.observation_spec)
|
|
print("action_spec:", env.action_spec)
|
|
print("reward_spec:", env.reward_spec)
|
|
print("done_spec:", env.done_spec)
|
|
|
|
td = env.reset()
|
|
print("reset tensordict", td)
|
|
|
|
td = env.rand_step(td)
|
|
print("random step tensordict", td)
|
|
|
|
def simple_rollout(steps=100):
|
|
# preallocate:
|
|
data = TensorDict({}, [steps])
|
|
# reset
|
|
_data = env.reset()
|
|
for i in range(steps):
|
|
_data["action"] = env.action_spec.rand()
|
|
_data = env.step(_data)
|
|
data[i] = _data
|
|
_data = step_mdp(_data, keep_other=True)
|
|
return data
|
|
|
|
print("data from rollout:", simple_rollout(100))
|
|
|
|
|
|
@pytest.mark.skip(reason="Simxarm is deprecated")
|
|
@pytest.mark.parametrize(
|
|
"task,from_pixels,pixels_only",
|
|
[
|
|
("lift", False, False),
|
|
("lift", True, False),
|
|
("lift", True, True),
|
|
("reach", False, False),
|
|
("reach", True, False),
|
|
("push", False, False),
|
|
("push", True, False),
|
|
("peg_in_box", False, False),
|
|
("peg_in_box", True, False),
|
|
],
|
|
)
|
|
def test_simxarm(task, from_pixels, pixels_only):
|
|
env = SimxarmEnv(
|
|
task,
|
|
from_pixels=from_pixels,
|
|
pixels_only=pixels_only,
|
|
image_size=84 if from_pixels else None,
|
|
)
|
|
# print_spec_rollout(env)
|
|
check_env_specs(env)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"from_pixels,pixels_only",
|
|
[
|
|
(True, False),
|
|
],
|
|
)
|
|
def test_pusht(from_pixels, pixels_only):
|
|
env = PushtEnv(
|
|
from_pixels=from_pixels,
|
|
pixels_only=pixels_only,
|
|
image_size=96 if from_pixels else None,
|
|
)
|
|
# print_spec_rollout(env)
|
|
check_env_specs(env)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"env_name",
|
|
[
|
|
# "simxarm",
|
|
"pusht",
|
|
"aloha",
|
|
],
|
|
)
|
|
def test_factory(env_name):
|
|
cfg = init_config(overrides=[f"env={env_name}"])
|
|
|
|
offline_buffer = make_offline_buffer(cfg)
|
|
|
|
env = make_env(cfg)
|
|
for key in offline_buffer.image_keys:
|
|
assert env.reset().get(key).dtype == torch.uint8
|
|
check_env_specs(env)
|
|
|
|
env = make_env(cfg, transform=offline_buffer.transform)
|
|
for key in offline_buffer.image_keys:
|
|
img = env.reset().get(key)
|
|
assert img.dtype == torch.float32
|
|
# TODO(rcadene): we assume for now that image normalization takes place in the model
|
|
assert img.max() <= 1.0
|
|
assert img.min() >= 0.0
|
|
check_env_specs(env)
|