Updates Mimic test cases to pytest format (#550)

# Description

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to check the contribution guidelines.

Link:
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Updates the following tests to pytest format:

- test_pink_ik.py
- test_selection_strategy.py
- test_generate_dataset.py

Updates test_generate_dataset.py to check that the expected number of
annotations are successfully generated in the HDF5 during the annotation
phase. If not, then test returns failure. This ensures that physics is
behaving correctly and that the correct annotated demos are being
generated. Previously, if a physics issue is introduced, the test would
timeout instead of failing.

The annotate_demos.py script was updated to return the number of
successfully annotated demos in order to support the above test.

## Type of change

<!-- As you go through the list, delete the ones that are not
applicable. -->

- New feature (non-breaking change which adds functionality)

## Checklist

- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [ ] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [x] I have added tests that prove my fix is effective or that my
feature works
- [ ] I have updated the changelog and the corresponding version in the
extension's `config/extension.toml` file
- [ ] I have added my name to the `CONTRIBUTORS.md` or my name already
exists there

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scripts/imitation_learning/isaaclab_mimic/annotate_demos.py

@@ -159,7 +159,7 @@ def main():
 
     if episode_count == 0:
         print("No episodes found in the dataset.")
-        exit()
+        return 0
 
     # get output directory path and file name (without extension) from cli arguments
     output_dir = os.path.dirname(args_cli.output_file)
@@ -236,6 +236,7 @@ def main():
     # simulate environment -- run everything in inference mode
     exported_episode_count = 0
     processed_episode_count = 0
+    successful_task_count = 0  # Counter for successful task completions
     with contextlib.suppress(KeyboardInterrupt) and torch.inference_mode():
         while simulation_app.is_running() and not simulation_app.is_exiting():
             # Iterate over the episodes in the loaded dataset file
@@ -259,6 +260,7 @@ def main():
                     )
                     env.recorder_manager.export_episodes()
                     exported_episode_count += 1
+                    successful_task_count += 1  # Increment successful task counter
                     print("\tExported the annotated episode.")
                 else:
                     print("\tSkipped exporting the episode due to incomplete subtask annotations.")
@@ -268,11 +270,16 @@ def main():
         f"\nExported {exported_episode_count} (out of {processed_episode_count}) annotated"
         f" episode{'s' if exported_episode_count > 1 else ''}."
     )
+    print(
+        f"Successful task completions: {successful_task_count}"
+    )  # This line is used by the dataset generation test case to check if the expected number of demos were annotated
     print("Exiting the app.")
 
     # Close environment after annotation is complete
     env.close()
 
+    return successful_task_count
+
 
 def replay_episode(
     env: ManagerBasedRLMimicEnv,
@@ -440,6 +447,8 @@ def annotate_episode_in_manual_mode(
 
 if __name__ == "__main__":
     # run the main function
-    main()
+    successful_task_count = main()
     # close sim app
     simulation_app.close()
+    # exit with the number of successful task completions as return code
+    exit(successful_task_count)

source/isaaclab/config/extension.toml

 [package]
 
 # Note: Semantic Versioning is used: https://semver.org/
-version = "0.42.24"
+version = "0.42.25"
 
 # Description
 title = "Isaac Lab framework for Robot Learning"

source/isaaclab/docs/CHANGELOG.rst

 Changelog
 ---------
 
+0.42.25 (2025-07-17)
+~~~~~~~~~~~~~~~~~~~~
+
+Changed
+^^^^^^^
+
+* Updated test_pink_ik.py test case to pytest format.
+
+
 0.42.24 (2025-06-25)
 ~~~~~~~~~~~~~~~~~~~~
 

source/isaaclab_mimic/config/extension.toml

 [package]
 
 # Semantic Versioning is used: https://semver.org/
-version = "1.0.10"
+version = "1.0.11"
 
 # Description
 category = "isaaclab"

source/isaaclab_mimic/docs/CHANGELOG.rst

 Changelog
 ---------
 
+1.0.11 (2025-07-17)
+~~~~~~~~~~~~~~~~~~
+
+Changed
+^^^^^^^
+
+* Updated test_selection_strategy.py and test_generate_dataset.py test cases to pytest format.
+* Updated annotate_demos.py script to return the number of successful task completions as the exit code to support check in test_generate_dataset.py test case.
+
+
 1.0.10 (2025-07-08)
 ~~~~~~~~~~~~~~~~~~
 

source/isaaclab_mimic/test/test_generate_dataset.py

@@ -13,110 +13,129 @@ simulation_app = AppLauncher(headless=True).app
 import os
 import subprocess
 import tempfile
-import unittest
+
+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")
-
-
-class TestGenerateDataset(unittest.TestCase):
-    """Test the dataset generation behavior of the Isaac Lab Mimic workflow."""
-
-    def setUp(self):
-        """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")
-            self.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
-        self.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 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
-        self.assertEqual(result.returncode, 0, msg=result.stderr)
-
-    def tearDown(self):
-        """Clean up after tests."""
-        if self.pythonunbuffered_env_var_:
-            os.environ["PYTHONUNBUFFERED"] = self.pythonunbuffered_env_var_
-        else:
-            del os.environ["PYTHONUNBUFFERED"]
-
-    def test_generate_dataset(self):
-        """Test the dataset generation script."""
-        # 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, "../../.."))
-
-        # 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
-        self.assertEqual(result.returncode, 0, msg=result.stderr)
-
-        # Check for specific output
-        expected_output = "successes/attempts. Exiting"
-        self.assertIn(expected_output, result.stdout)
-
-
-if __name__ == "__main__":
-    unittest.main()
+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"]
+
+
+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