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)
31 lines
1007 B
Python
31 lines
1007 B
Python
import pytest
|
|
import torch
|
|
|
|
from lerobot.common.datasets.factory import make_offline_buffer
|
|
|
|
from .utils import init_config
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"env_name,dataset_id",
|
|
[
|
|
# TODO(rcadene): simxarm is depreciated for now
|
|
# ("simxarm", "lift"),
|
|
("pusht", "pusht"),
|
|
# TODO(aliberts): add aloha when dataset is available on hub
|
|
("aloha", "sim_insertion_human"),
|
|
("aloha", "sim_insertion_scripted"),
|
|
("aloha", "sim_transfer_cube_human"),
|
|
("aloha", "sim_transfer_cube_scripted"),
|
|
],
|
|
)
|
|
def test_factory(env_name, dataset_id):
|
|
cfg = init_config(overrides=[f"env={env_name}", f"env.task={dataset_id}"])
|
|
offline_buffer = make_offline_buffer(cfg)
|
|
for key in offline_buffer.image_keys:
|
|
img = offline_buffer[0].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
|