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406662d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | # Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Launch Isaac Sim Simulator first."""
from isaaclab.app import AppLauncher
# launch omniverse app in headless mode
simulation_app = AppLauncher(headless=True).app
"""Rest everything follows from here."""
import os
import shutil
import tempfile
import uuid
import pytest
import torch
from isaaclab.utils.datasets import EpisodeData, HDF5DatasetFileHandler
def create_test_episode(device):
"""create a test episode with dummy data."""
test_episode = EpisodeData()
test_episode.seed = 0
test_episode.success = True
test_episode.add("initial_state", torch.tensor([1, 2, 3], device=device))
test_episode.add("actions", torch.tensor([1, 2, 3], device=device))
test_episode.add("actions", torch.tensor([4, 5, 6], device=device))
test_episode.add("actions", torch.tensor([7, 8, 9], device=device))
test_episode.add("obs/policy/term1", torch.tensor([1, 2, 3, 4, 5], device=device))
test_episode.add("obs/policy/term1", torch.tensor([6, 7, 8, 9, 10], device=device))
test_episode.add("obs/policy/term1", torch.tensor([11, 12, 13, 14, 15], device=device))
return test_episode
@pytest.fixture
def temp_dir():
"""Create a temporary directory for test datasets."""
temp_dir = tempfile.mkdtemp()
yield temp_dir
# cleanup after tests
shutil.rmtree(temp_dir)
def test_create_dataset_file(temp_dir):
"""Test creating a new dataset file."""
# create a dataset file given a file name with extension
dataset_file_path = os.path.join(temp_dir, f"{uuid.uuid4()}.hdf5")
dataset_file_handler = HDF5DatasetFileHandler()
dataset_file_handler.create(dataset_file_path, "test_env_name")
dataset_file_handler.close()
# check if the dataset is created
assert os.path.exists(dataset_file_path)
# create a dataset file given a file name without extension
dataset_file_path = os.path.join(temp_dir, f"{uuid.uuid4()}")
dataset_file_handler = HDF5DatasetFileHandler()
dataset_file_handler.create(dataset_file_path, "test_env_name")
dataset_file_handler.close()
# check if the dataset is created
assert os.path.exists(dataset_file_path + ".hdf5")
@pytest.mark.parametrize("device", ["cuda:0", "cpu"])
def test_write_and_load_episode(temp_dir, device):
"""Test writing and loading an episode to and from the dataset file."""
dataset_file_path = os.path.join(temp_dir, f"{uuid.uuid4()}.hdf5")
dataset_file_handler = HDF5DatasetFileHandler()
dataset_file_handler.create(dataset_file_path, "test_env_name")
test_episode = create_test_episode(device)
# write the episode to the dataset
test_episode.pre_export()
dataset_file_handler.write_episode(test_episode)
dataset_file_handler.flush()
assert dataset_file_handler.get_num_episodes() == 1
# write the episode again to test writing 2nd episode
dataset_file_handler.write_episode(test_episode)
dataset_file_handler.flush()
assert dataset_file_handler.get_num_episodes() == 2
# close the dataset file to prepare for testing the load function
dataset_file_handler.close()
# load the episode from the dataset
dataset_file_handler = HDF5DatasetFileHandler()
dataset_file_handler.open(dataset_file_path)
assert dataset_file_handler.get_env_name() == "test_env_name"
loaded_episode_names = dataset_file_handler.get_episode_names()
assert len(list(loaded_episode_names)) == 2
for episode_name in loaded_episode_names:
loaded_episode = dataset_file_handler.load_episode(episode_name, device=device)
assert loaded_episode.env_id == "test_env_name"
assert loaded_episode.seed == test_episode.seed
assert loaded_episode.success == test_episode.success
assert torch.equal(loaded_episode.get_initial_state(), test_episode.get_initial_state())
for action in test_episode.data["actions"]:
assert torch.equal(loaded_episode.get_next_action(), action)
dataset_file_handler.close()
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