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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | # Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: Apache-2.0
"""Test dataset generation for Isaac Lab Mimic workflow."""
from isaaclab.app import AppLauncher
# launch omniverse app
simulation_app = AppLauncher(headless=True).app
import os
import subprocess
import tempfile
import pytest
from isaaclab.utils.assets import ISAACLAB_NUCLEUS_DIR, retrieve_file_path
DATASETS_DOWNLOAD_DIR = tempfile.mkdtemp(suffix="_Isaac-Stack-Cube-Franka-IK-Rel-Mimic-v0")
NUCLEUS_DATASET_PATH = os.path.join(ISAACLAB_NUCLEUS_DIR, "Tests", "Mimic", "dataset.hdf5")
EXPECTED_SUCCESSFUL_ANNOTATIONS = 10
@pytest.fixture
def setup_test_environment():
"""Set up the environment for testing."""
# Create the datasets directory if it does not exist
if not os.path.exists(DATASETS_DOWNLOAD_DIR):
print("Creating directory : ", DATASETS_DOWNLOAD_DIR)
os.makedirs(DATASETS_DOWNLOAD_DIR)
# Try to download the dataset from Nucleus
try:
retrieve_file_path(NUCLEUS_DATASET_PATH, DATASETS_DOWNLOAD_DIR)
except Exception as e:
print(e)
print("Could not download dataset from Nucleus")
pytest.fail(
"The dataset required for this test is currently unavailable. Dataset path: " + NUCLEUS_DATASET_PATH
)
# Set the environment variable PYTHONUNBUFFERED to 1 to get all text outputs in result.stdout
pythonunbuffered_env_var_ = os.environ.get("PYTHONUNBUFFERED")
os.environ["PYTHONUNBUFFERED"] = "1"
# Automatically detect the workflow root (backtrack from current file location)
current_dir = os.path.dirname(os.path.abspath(__file__))
workflow_root = os.path.abspath(os.path.join(current_dir, "../../.."))
# Run the command to generate core configs
config_command = [
workflow_root + "/isaaclab.sh",
"-p",
os.path.join(workflow_root, "scripts/imitation_learning/isaaclab_mimic/annotate_demos.py"),
"--task",
"Isaac-Stack-Cube-Franka-IK-Rel-Mimic-v0",
"--input_file",
DATASETS_DOWNLOAD_DIR + "/dataset.hdf5",
"--output_file",
DATASETS_DOWNLOAD_DIR + "/annotated_dataset.hdf5",
"--auto",
"--headless",
]
print(config_command)
# Execute the command and capture the result
result = subprocess.run(config_command, capture_output=True, text=True)
print(f"Annotate demos result: {result.returncode}\n\n\n\n\n\n\n\n\n\n\n\n")
# Print the result for debugging purposes
print("Config generation result:")
print(result.stdout) # Print standard output from the command
print(result.stderr) # Print standard error from the command
# Check if the config generation was successful
assert result.returncode == 0, result.stderr
# Check that at least one task was completed successfully by parsing stdout
# Look for the line that reports successful task completions
success_line = None
for line in result.stdout.split("\n"):
if "Successful task completions:" in line:
success_line = line
break
assert success_line is not None, "Could not find 'Successful task completions:' in output"
# Extract the number from the line
try:
successful_count = int(success_line.split(":")[-1].strip())
assert successful_count == EXPECTED_SUCCESSFUL_ANNOTATIONS, (
f"Expected 10 successful annotations but got {successful_count}"
)
except (ValueError, IndexError) as e:
pytest.fail(f"Could not parse successful task count from line: '{success_line}'. Error: {e}")
# Yield the workflow root for use in tests
yield workflow_root
# Cleanup: restore the original environment variable
if pythonunbuffered_env_var_:
os.environ["PYTHONUNBUFFERED"] = pythonunbuffered_env_var_
else:
del os.environ["PYTHONUNBUFFERED"]
@pytest.mark.isaacsim_ci
def test_generate_dataset(setup_test_environment):
"""Test the dataset generation script."""
workflow_root = setup_test_environment
# Define the command to run the dataset generation script
command = [
workflow_root + "/isaaclab.sh",
"-p",
os.path.join(workflow_root, "scripts/imitation_learning/isaaclab_mimic/generate_dataset.py"),
"--input_file",
DATASETS_DOWNLOAD_DIR + "/annotated_dataset.hdf5",
"--output_file",
DATASETS_DOWNLOAD_DIR + "/generated_dataset.hdf5",
"--generation_num_trials",
"1",
"--headless",
]
# Call the script and capture output
result = subprocess.run(command, capture_output=True, text=True)
# Print the result for debugging purposes
print("Dataset generation result:")
print(result.stdout) # Print standard output from the command
print(result.stderr) # Print standard error from the command
# Check if the script executed successfully
assert result.returncode == 0, result.stderr
# Check for specific output
expected_output = "successes/attempts. Exiting"
assert expected_output in result.stdout
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